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LI Demin, SHAN Xin, WANG Yi, YANG Ke, FENG Qiuxia
Available online:June 25, 2025 DOI: 10.7500/AEPS20240624004
Abstract:In response to the problems of high solving complexity, long calculation time and inconsistent results in the current distribution network optimization reconstruction, which make it difficult to implement in engineering, a two-stage topology optimization reconstruction strategy of distribution network is proposed combined graph search algorithm. In first stage, graph search and path-combination strategy is proposed to generate distribution network reconstruction structural set, which can determine the branches with on-off state changes before optimizing. This helps to narrow down the selection range of structural decision variables and improve the calculation speed when solving the reconstruction model. In second stage, a model solving method for target value sorting based on deterministic topology is proposed based on the distribution network reconstruction structural set and combined with cone transformation technology, which realizes the decomposition of distribution network optimization reconstruction into two parts: topology structure generation based on 0-1 state variables and linear model solving based on determined topology, thus greatly reducing the complexity of model solving. A case study is conducted on a 64-node system of a certain regional power gird, and the results show that the proposed distribution network optimization reconstruction method can effectively obtain the optimal topology structure that meet user needs, which is very useful for practical engineering applications.
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YE Lin, LV Kexin, PEI Ming, LUO Yadi, SONG Xuri, SHA Licheng
Available online:June 25, 2025 DOI: 10.7500/AEPS20240806001
Abstract:Traceability of power prediction errors can analyse the causes of prediction errors, distinguish the importance of factors affecting prediction errors, and improve the function of prediction models. PV power prediction error is greatly affected by the level of Numerical Weather Prediction (NWP), and affected by different NWP weather elements, it is difficult to quantitatively trace back to a variety of NWP data. For this reason, the article proposes a method to trace the PV power prediction error considering the causal verification of NWP transfer entropy. Firstly, the PV power prediction error is divided into trends using the Swing Windows (SW) algorithm, while the frequency domain is decomposed using the Variational Modal Decomposition (VMD) algorithm to obtain the characteristic segments of the PV power prediction error. Finally, the causal relationship between different kinds of NWP data and PV power prediction errors is verified by using transfer entropy, and the error contribution of NWP data to each PV power prediction error characteristic segment is analysed retrospectively. Examples show that the proposed method can effectively trace the PV power prediction error caused by NWP data.
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YAN Gangui, SHA Qianli, LI Junhui, ZHAN Shuo, LI Yongyue, QIAO Xin
Available online:June 25, 2025 DOI: 10.7500/AEPS20240926005
Abstract:Aiming at the problems of unreasonable frequency regulation demand setting, low life of battery energy storage and poor economy in the frequency regulation capacity configuration of thermal power units assisted by battery energy storage system (BESS), a regulation performance-oriented method for determining the demand of hybrid thermal power-energy storage frequency regulation is designed, and a regulation performance-oriented bi-level optimal configuration strategy of hybrid energy storage system (HESS) for thermal power-energy storage frequency regulation is proposed. In operation inner layer, based on the HESS configuration given in the economic outer layer, the regulation performance-oriented HESS frequency regulation response demand is optimized with the minimum BESS life loss and the maximum comprehensive frequency regulation performance index as the objectives, and the BESS life loss and frequency regulation performance are weighed. In the economic outer layer, based on the regulation performance-oriented HESS frequency regulation response demand and HESS response effect after the optimization of the operation inner layer, the HESS capacity configuration is optimized with the goal of maximizing the average annual net income to achieve the optimal comprehensive benefit. The optimization algorithm is used to solve the bi-level model to obtain an optimal configuration scheme that takes into account both frequency regulation performance and economic benefits. Finally, based on operation data of a 330 MW-rated unit for one month, the effectiveness and superiority of the proposed configuration strategy are verified.
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Weifeng LIU, Shen YU, Zhihua WANG, Yuhang MENG, Qing WU, Guangyu HE
Available online:June 25, 2025 DOI: 10.7500/AEPS20250109001
Abstract:To enable electrical appliances to actively adapt to changes in electricity demand and operational environments like intelligent robots, this paper proposes a construction method for autonomous agents of electrical appliances utilizing a technology roadmap based on state transitions. These agents are capable of providing appropriate electrical services in scenarios of intelligent electricity use and demand response. Initially, the concept of compatibility and its evaluation method are introduced from the perspective of the appliances, to quantify the alignment between the services provided by the appliances and the electricity demand and operational environments. Subsequently, a series of functional states are defined and delineated according to the value provided by these electricity services. These states form the foundation for autonomously optimizing the performance of the electrical appliance agents across various usage scenarios. Next, a self-approaching optimization operation method driven by these functional states is designed, employing self-recognition and self-transition techniques to ensure that the appliance agents perpetually strive towards an optimal operational state. Finally, utilizing the autonomous decentralized system theory, a simulation framework for these electrical appliance agents is developed and implemented on 10,000 lights and inverter air conditioners to verify the effectiveness of the proposed methods. The performance of these agents and the impact of various driving techniques are analyzed, demonstrating the superiority of our approach. Furthermore, the practical effect of these autonomous agents in intelligent electricity usage and demand response scenarios is evaluated, providing quantitative results that can underpin engineering implementation and policy development.
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ZUO Juan, AI Qian, WANG Wenbo, ZHAOJianli, YU Tao, TAO Weijian
Available online:June 25, 2025 DOI: 10.7500/AEPS20240925001
Abstract:As an innovative energy management system integrating distributed energy resources, the virtual power plant (VPP) has gained significant global attention. To promote the standardization of VPP, various countries and international standardization organizations have developed relevant standards and guidelines, providing standardized references for the planning, construction, operation, and maintenance of VPP, thereby advancing its application and development. Firstly, this paper reviews the current state of VPP standardization both domestically and internationally. Secondly, in line with the dynamic trends in VPP business evolution, it analyzes the key issues and challenges in the standardization efforts. Thirdly, based on the goal of establishing a national VPP standardization framework, this paper proposes a comprehensive VPP standard system framework encompassing two primary directions and seven key domains, and also plans the key development roadmap for the VPP standard system. Finally, this paper puts forward suggestions for further improving and standardizing the VPP standard system, aiming to serve as a reference for the formulation and implementation of VPP standards and contribute to its regulated and efficient development.
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ZHANG Qiang, CAO Boran, LIU Hong, HAN Jun, CAI Chao, PAN Wenjie
Available online:June 25, 2025 DOI: 10.7500/AEPS20240511007
Abstract:Aiming at the issue that utilizing the mobile energy storage characteristics of electric vehicles (EVs) to achieve multi-spatio-temporal load balancing and efficient photovoltaic accommodation in the distribution network, this paper proposes a day-ahead interaction method among EVs, the electric vehicle agent (EVA) and the distribution network. First, a day-ahead interactive operation framework for EVs, EVA, and the distribution network operator (DNO) considering the spatio-temporal characteristics of EVs is established. Considering the economic cost-loss model of battery charging or discharging per unit of energy at different discharge depths, this paper constructs a bi-level interactive model integrating a time-of-use price optimization model based on differentiated load characteristics of feeders for a DNO and an embedded EVA scheduling model. In the model, DNO optimizes the differentiated time-of-use electricity price of each feeder to maximize its revenue under the premise of considering the maximum power constraint of the distribution network. EVA optimizes the charging and discharging schedule of EVs to maximize the sum of EV charging and discharging revenue and the peak regulation subsidy given by the distribution network. Then, the discharging threshold constraint is transformed into a linear constraint, and a solving method based on the sample boundary compression algorithm and CPLEX solver is proposed. Finally, the effectiveness of the proposed model and method is verified through the numerical simulation.
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YAO Chenhao, XIONG XiaoLing, FENG DingTeng, LI XiaoBo, ZHAO ChengYong
Available online:June 24, 2025 DOI: 10.7500/AEPS20240303001
Abstract:Current source converter (CSC) offshore wind power transmission system has the advantages of high power density and power supply to passive network, so it has a good application prospect. However, the existing scheme has three problems: it needs a lot of communication, black start is uneconomical and the reactive power of the fan is easy to overload. In this paper, an improved island operation and black startup strategy is proposed. Firstly, based on the sensitivity analysis, a method of cooperative control of AC voltage amplitude and frequency by CSC and fan without communication is proposed. Then, a method is proposed to build AC voltage at sea by using LC filter as reactive load and CSC at the sending end and CSC at the receiving end. Then, the influence of DC voltage on reactive power overload is analyzed, and the reactive power optimization strategy is proposed. Then, the above strategies are verified by simulation based on PSCAD. Finally, the CSC scheme and DRU scheme are discussed from the aspects of complexity, feasibility and cost. The results show that the improved island operation and black startup strategy are effective.
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QI Donglian, Chen Yulin, ZHU Yizhe, LI Li, YAN Yunfeng, LIN Zhenzhi
Available online:June 24, 2025 DOI: 10.7500/AEPS20241226006
Abstract:Power system is one of the most critical infrastructure in a country, thus ensuring its secure and stable operation is of great significance. However, power system may also face unconventional extreme threats such as malicious strikes under complex international situations. Compared to extreme weather events such as typhoons, unconventional extreme events have features of intentionality, strong suddenness, and high destructiveness, posing a great threat to the secure and stable operation of the power system. Traditional system protection measures would be difficult to cope with. Therefore, from the perspective of unconventional events, this paper explores the extreme survival issue of power systems under unconventional extreme events. Firstly, based on the characteristics of unconventional extreme events, the concept of power system invulnerability is proposed, which is defined as: the maximum power supply capacity that the power system can maintain after experiencing a certain degree of unconventional extreme events. And it was compared in detail with concepts of reliability, self-healing, and resilience; Subsequently, the problems and challenges faced by the power system under unconventional extreme events are analyzed, and the research concepts and plan are preliminarily sorted out. Five urgent research directions have been proposed: unconventional extreme events simulation and modeling of system damage mechanisms, assessment of power system survivability to unconventional extreme events, improvement strategies of power system survivability from the perspective of information physics coupling, support systems and equipment for power system damage detection and repair, and a sand table simulation system for power system survivability to unconventional extreme events. The main purpose of this paper is to point out the importance and urgency of studying methods to improve the invulnerability of power systems under unconventional extreme events, and propose forward-looking research ideas, which can help enhance the ultimate survival capacity of our power system.
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TANG Xisheng, LOU Yantao, DAI Xingjian, WANG Taian, SUN Danan, QIU Ming
2025,49(11):1-13, DOI: 10.7500/AEPS20240424010
Abstract:With the continuous increase in the proportion of renewable energy generation and power electronic equipment connected to the power grid, the grid-forming demand such as inertia response, fault transient support, rapid frequency and voltage regulation of the power system has become prominent. As a short-term high-power physical energy storage technology, the flywheel energy storage has broad prospects for its application in the grid-forming operation with rapid high-frequency regulation in the power system. This paper mainly introduces the current development and application status of the high-speed flywheel energy storage technology. Based on the three core parameters of power, energy storage capacity, and rotational speed, a comprehensive index of the “technical strength” of flywheel energy storage is proposed. Using the index as a reference, the development trend of flywheel energy storage technology at home and abroad is analyzed. The technical problems that need to be solved in such aspects as magnetic suspension bearings, high-strength rotors, vacuum heat dissipation, and shaft vibration suppression for flywheel energy storage single machines are proposed. It is pointed out that the optimization of assembly architecture, collaborative control and energy management is necessary for the grid-forming operation of flywheel array, and the overall response ability and transient characteristics of the array need to be improved.
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2025,49(11):14-28, DOI: 10.7500/AEPS20240326001
Abstract:The grid-forming energy
s torage conversion system (ESCS) can enhance the accommodation level of renewable energy, which is the critical device for stabilizing the renewable energy power system. Establishing and improving quantitative indicators and standardized testing methods for the grid-forming capability of ESCS is the prerequisite for the large-scale engineering application of grid-forming technology. Countries around the world are systematically developing technical standards and promoting projects. Regarding the functional classification, technical indicators, and compliance verification of the grid-forming ESCS, this paper systematically reviews domestic and international white papers, technical standards, and technical reports. The quantitative performance indicators, functional test contents, and technical specification requirements for the grid-forming ESCS in domestic and international standards are described. The technical connotations of indicators such as the active responses of the rate of change of frequency (RoCoF), inertia, phase jump, and voltage jump are interpreted. This paper analyzes the multi-level electromagnetic transient (EMT) simulation modeling steps and requirements for verifying the quantitative performance indicators of grid-forming ESCS in specific application scenarios, as well as the online monitoring data requirements for grid-forming ESCS in projects. Then, current relevant standards and local policies in China and twelve types of grid-forming functions and parameter requirements are summarized. Finally, the optimization of technical standards and the research directions for the grid-forming ESCS are prospected, which provides references for the promotion and large-scale engineering application of the grid-forming ESCS technology. -
YAN Gangui, XING Aolan, MU Gang, CHEN Lu, ZHAO Jule, ZHAN Shuo
2025,49(11):29-36, DOI: 10.7500/AEPS20240430016
Abstract:In power systems with high proportion of renewable energy, the uncertainty of injected power leads to difficulties in power balance. Recognizing the uncertainty of injected power in the system is the key to rationally allocating and regulating resources and ensuring the balance of supply and demand in the system. From the perspective of system operation and taking the daily power waveform as the research element, a quantitative evaluation and analysis method for uncertainty of daily power waveform based on benchmark mode is proposed. The instability degree of fluctuations of the daily power waveform is measured according to the deviation between the daily power waveform and the benchmark-mode power waveform. Based on this, the uncertainty characteristics of multiple types of daily power waveforms such as load, wind power, and photovoltaic are analyzed. Based on the deviation between the total passive power waveform after the clustering of multiple types of daily power waveforms and its benchmark mode power waveform, the demand of system regulation power and regulation electricity brought by the interconnection of wind power and photovoltaic power is quantitatively evaluated, providing theoretical guidance for the demand evaluation of regulation resource in power systems with high proportion of renewable energy.
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LUO Renjie, JU Jiaxin, LI Zhiyi
2025,49(11):37-47, DOI: 10.7500/AEPS20240906008
Abstract:Considering the rapid increase of flexibility demand characterized by multi-timescale fluctuations in the new power system, the traditional separate quantization method for the regulation capacity such as frequency regulation and ramp may cause excessive capacity reservation. It is urgent to explore the resource regulation performance and quantify the system flexibility demand to improve the safe operation of power systems. This paper proposes a flexibility demand decomposition of power system and multi-timescale regulation capacity reservation method based on hybrid mathematical morphology decomposition. Firstly, the kernel density distribution model is used to generate the set of flexibility demand curves within the prediction interval. Secondly, the hybrid mathematical morphology filter considering the output characteristics of regulation resources is designed to decompose the flexibility demand curves into regular and random components. The needed frequency regulation capacity is then calculated according to the set of random components. Finally, based on the regular components obtained by the flexibility demand decomposition, the stochastic optimization model is constructed for long- and short-term ramping capacity reservation of regulation resources. Numerical results show that, compared with traditional methods, the proposed model can match the decomposition results with the output characteristics of regulation resources. Meanwhile, the reservation scheme can meet the flexibility demand of power systems and enhance the capacity utilization rate.
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2025,49(11):48-58, DOI: 10.7500/AEPS20240331011
Abstract:With the advancement of the goal of “carbon emission peak and carbon neutrality” and the increase of demand for user satisfaction, the contradiction between power-side and load-side supply and demand is intensifying. It is crucial to achieve optimal operation of the supply and demand interaction in the power system based on low-carbon emissions. In order to achieve low-carbon emissions in the system and improve the user satisfaction, a hierarchical optimal dispatch model considering wind-solar-storage-carbon synergy on the power side and user satisfaction on the load side is proposed. Firstly, the power side takes the optimal economy as the objective and uses the dynamically constrained carbon emission model to optimize the constraints of thermal power units. Secondly, the load side considers the time-of-use tariffs, and takes the maximum user satisfaction level as the objective function. Then, the hybrid optimization operation based on improved salp swarm algorithm and arithmetic optimization algorithm, as well as particle swarm algorithm are used to perform iterative operations on the hierarchical model, thus achieving the optimal integrated level of dispatch results. Finally, the proposed model is verified through an improved IEEE 33-bus system. Results demonstrate that the established model can not only improve the utilization rate of wind-solar renewable energy, enhance the accommodation ability of energy storage to renewable energy, and limit the power generation of high-carbon-emission units, but also maximize the user satisfaction level, which provides theoretical support for the realization of low-carbon goal.
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QIN Xuexue, LI Zening, XUE Yixun, CHANG Xinyue, SU Jia
2025,49(11):59-69, DOI: 10.7500/AEPS20240706001
Abstract:To achieve the economic and flexible operation of data centers, a two-stage robust optimization method for spatio-temporal coordination of data center considering thermal dynamic characteristics of buildings is proposed. Firstly, based on the thermal dynamic characteristics of data center buildings and the differentiated real-time requirements of cloud user loads, a spatio-temporal coordination mathematical model of cloud user loads in data centers considering the operation environment temperature of servers is constructed. Secondly, considering the uncertainties of conventional load and outdoor temperature, a two-stage robust optimization method for data center considering the thermal dynamic characteristics of data center buildings is proposed. The degree of optimization conservatism is flexibly adjusted by setting uncertainty adjustment parameters. Then, the column and constraint generation (C&CG) algorithm is adopted for iterative solution to obtain the optimal solution to the original problem. Finally, the proposed method is verified through case analysis. The results show that the proposed method can fully tap the synergy potential of the spatio-temporal flexibility of cloud user loads and the flexibility of air conditioning cooling on the premise of ensuring the operation environment temperature of the server, significantly improve the economy of data center operation, and effectively enhance the system ability to resist risks.
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LI Xiongfei, LI Kangping, HUANG Chunyi, AI Qian, HUAI Wenming, ZHANG Yuyang
2025,49(11):70-79, DOI: 10.7500/AEPS20240804002
Abstract:Load aggregators (LAs) can aggregate numerous residential users to provide demand response (DR) services for power grids by participating in DR market transactions. Existing research typically treats LAs’ bidding on the market side and their pricing on the user side as two separate issues without linkage. Moreover, the uniform incentive pricing is commonly used on the user side, neglecting the differences in incentive response behaviors among individual users. These two issues pose significant risks of profit loss for LAs. Therefore, this paper proposes a two-stage joint bidding and pricing optimization method considering differences of user response behaviors. In the first stage, considering the uncertainties of user response behavior and clearing prices, a joint bidding and pricing optimization model is established for LAs using stochastic optimization methods. In the second stage, based on the actual clearing results and considering the differences of user response behavior, robust optimization is adopted to implement differentiated adjustments to pre-pricing schemes. Case study demonstrates that the proposed method can significantly improve LAs’ expected revenue in the DR market while reducing transaction risks
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LIU Chengjun, LOU Qi, XU Yijun, GU Wei, WEN Kai, MA Yin
2025,49(11):80-90, DOI: 10.7500/AEPS20241006002
Abstract:The implementation of a partitioning parallel restoration strategy after a power grid blackout can ensure the rapid restoration of normal power grid operation, while the accurate and efficient partitioning method is one of the important prerequisites for implementing recovery strategies. Faced with the increasingly strict time-sensitive requirements of the new power system, existing methods encounter three main limitations: inability to directly solve models, dependence on manual selection of solutions, and constraints imposed by classical computer processing capabilities. To address the above problems, this paper leverages emerging quantum computing technology and proposes a quantum computing based partitioning method for the rapid restoration of power grids after blackouts. First, the network partitioning model after a large-scale blackout is constructed considering the actual operation security constraints to minimize the sum of weights of the excised lines, which is transformed into a quadratic unconstrained binary optimization binary model that can be directly solved by the optical quantum computer. Secondly, the possibility of qubit expansion methods based on sub-problem extraction is preliminarily explored considering the real qubit limitation of the coherent Ising machine. Finally, relying on specialized quantum computers, the effectiveness of both the proposed partitioning model and quantum expansion method is validated through two power systems with different scales.
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LI Qun, YIN Minghui, LIU Kunlong, CHEN Zaiyu, LI Qiang, ZOU Yun
2025,49(11):91-101, DOI: 10.7500/AEPS20240711003
Abstract:For grid-forming wind turbines (WTs), virtual synchronous control provides inertia support to the power system by simulating the operation principle of synchronous generators through the virtual rotor. Although the virtual rotor speed is synchronized with the system frequency, it is found that due to differences in rotor driving power, the physical rotor (wind rotor) of permanent magnet WTs cannot actually maintain synchronization with the system frequency through the virtual rotor. As a result, grid-forming WTs also face the same problem as grid-following WTs, that is, it cannot provide definite and continuous inertia support to the power system. In particular, the inertia support of grid-forming WTs is usually terminated before the system frequency reaches the extreme point. This severely affects the frequency response characteristics of power system with high proportion of wind power. To address this issue, this paper proposes the definition of the non-synchronous rotor inertia time coefficient, under which the time-varying characteristics of the inertia time coefficient for grid-forming WTs using virtual synchronous control are analyzed. On this basis, a physical rotor binding method is proposed for grid-forming WTs to achieve consistent changes between the physical rotor speed and the system frequency, enabling grid-forming WTs to provide constant inertia support to the system and improve the frequency response characteristics of power system with high proportion of wind power.
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HE Ping, WANG Mingyang, WEN Fushuan, LI Qiuyan, PAN Zhiwen, LIU Zemeng
2025,49(11):102-113, DOI: 10.7500/AEPS20241121004
Abstract:The energy storage virtual synchronous generator (VSG) can effectively enhance the regulation capability of the power grid. However, its inherent oscillation characteristics also bring significant risks to the stable operation of the power grid. To address this situation, this paper establishes a small-signal model of VSG and power system considering the influence of virtual excitation regulator (VER) and virtual compensation link, to identify the key variables affecting the system stability. Then the influence of VER and virtual power compensation (VPC) on the system damping characteristics is analyzed by using the complex torque coefficient method. It is found that VER will introduce the negative damping torque. Consequently, the power decoupling control for the energy storage VSG is designed to eliminate the negative damping characteristics of the VER. Furthermore, the analysis indicates that the damping torque provided by VPC to the system is affected by the oscillation frequency, and there are mutual constraints among different control objectives, limiting the ability of VPC to improve the system damping characteristics. Therefore, a VSG control based on virtual frequency compensation (VFC) is proposed, which improves the system damping characteristics without affecting its dynamic response and eliminates the influence of oscillation frequency on damping torque. Finally, the accuracy of the theoretical analysis and the effectiveness of the proposed control are verified by simulation.
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XIA Xiangyang, ZHAO Xiaoyue, LIANG Jun, LUO Wenguang, CHEN Guiquan, ZHENG Hanfeng
2025,49(11):114-125, DOI: 10.7500/AEPS20240905002
Abstract:Aiming at the issues of transient power angle instability and fault current over-limit of grid-forming converters during the symmetrical fault of the power grid, this paper proposes a joint control method integrating second-order sliding mode control and adaptive voltage regulation. First, an improved integral sliding mode surface is introduced in the active power-frequency loop of the grid-forming converter, and a Lyapunov function is constructed. Based on the stability criterion, the output power control law is designed to strictly limit the fluctuation of output power angle of the converter during faults while considering power grid phase changes. Furthermore, the reactive power-voltage loop switching control is employed to provide stable reactive power for supporting the voltage at the point of common coupling, which ensures that fault currents are maintained within safe thresholds. Finally, simulation and experimental results show that the proposed strategy can effectively enhance the fault ride-through capability of grid-forming converters and the recovery rate after fault clearance under the condition of a severe voltage drop with phase jump in the power grid. Its refined active support characteristics are also applicable to complex conditions of high and low voltage ride-through.
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LI Chao, LI Borui, LI Bin, LI Xindong, WU Tonghua
2025,49(11):126-135, DOI: 10.7500/AEPS20240602001
Abstract:Under the goal of “carbon emission peak and carbon neutrality”, the electrochemical energy storage power stations are rapidly developing. However, due to the influence of converter control strategies, the fault characteristics of electrochemical energy storage power stations are significantly different from traditional synchronous power sources. Meanwhile, the energy storage stations have charging mode and discharging mode, which makes their fault characteristics different from those of renewable energy sources such as wind and photovoltaic. Therefore, the connection of electrochemical energy storage power stations will have a serious impact on the power directional elements of the distribution network. First, the fault characteristics of electrochemical energy storage power stations are studied. On this basis, the impact mechanism of fault characteristics of electrochemical energy storage power stations under charging/discharging operation mode on the directional elements of power grid protection is analyzed. Then, the fault identification method based on the principle of current selective tripping protection is proposed to effectively eliminate the misjudgment impact of the connection of electrochemical energy storage power stations on the power directional elements. Finally, the verification is conducted based on the PSCAD/EMTDC simulation platform. The results indicate that the phase difference variation between short-circuit current and voltage during the charging of energy storage stations leads to the incorrect operation of power directional elements on the side of the energy storage stations. However, the proposed protection scheme is not affected by the charging mode of the energy storage power stations and can quickly and correctly identify the fault location.
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YE Dongmeng, HAO Zhiguo, XIE Fan, LIU Wei, WANG Ting
2025,49(11):136-146, DOI: 10.7500/AEPS20230908003
Abstract:Highly integrated DC microgrid systems are characterized by complex fault types and low communication costs. Model-based centralized fault diagnosis methods offer an effective solution to fault diagnosis in DC microgrids. This paper establishes a switching system model of DC microgrids with converters, and quantitatively describes the impacts of various internal and external faults of high-power converters on the switching system from the perspective of model parameter variations. Subsequently, through optimal design of observer parameters, each observer is configured to exhibit high selectivity toward different faults. A one-to-one mapping of fault feature codes is then employed to achieve precise fault recognition. Simulation verification shows that the proposed method can achieve reliable recognition of several common faults in DC microgrids, meet the speed requirements of DC microgrid fault diagnosis, and have good robustness against modeling errors, transition resistance, and measurement noise.
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LIU Weifeng, SHEN YU, WANG Zhihua, MENG Yuhang, WU Qing, HE Guangyu
2025,49(11):147-158, DOI: 10.7500/AEPS20240926002
Abstract:The smart home automation control system needs to adapt to dynamic application scenarios in terms of electricity demand and operation environment, posing challenges to system accuracy, responsiveness, and adaptability. Therefore, an adaptive control method for smart homes in dynamic application scenarios is proposed. Firstly, the concept of electricity usage scenario (EUS) is introduced to uniformly describe various application scenarios, with standard EUS serving as the foundation for continuous optimization and control of electrical appliances. Simultaneously, an adaptive parameter optimization method based on user feedback is proposed to ensure system adaptation to time-varying electricity demands. Secondly, a compatibility assessment method considering spatio-temporal characteristics is proposed to evaluate the alignment among the operation states of electrical appliances, electricity demands, and operation environment. Then, a compatibility-driven consistency control method is proposed to enable smart homes to satisfy electricity demands and achieve the control objectives in time-varying environments. Finally, a smart home simulation system is developed using SimPy to test the proposed methods in controlling lighting, inverter air conditioners, and humidifiers. The results demonstrate that the proposed method enables the smart home automation control system to meet the electricity demands accurately, responsively, and continuously. Compared to traditional compatibility assessment and automatic control methods, the proposed method can significantly improve the misoperation rate, user-friendliness, and error control rate.
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LIANG Beihua, XIE Huan, ZHAO Tianqi, LIANG Hao, HAO Jing, XIN Huanhai
2025,49(11):159-167, DOI: 10.7500/AEPS20240807001
Abstract:The integration of distributed synchronous condensers into renewable energy stations can effectively improve the short-circuit ratio index and the system transmission capability. However, the influence and quantitative evaluation of the control characteristics of synchronous condensers on the voltage-supporting capability and the power grid strength are rarely studied. First, this paper explains the requirements of the voltage-supporting function of the station throughout the entire process. Based on the frequency-domain index, the difference in voltage-supporting capability of synchronous condensers with two strategies of voltage control and cascaded reactive power outer loop control is compared and analyzed. It is revealed that the introduction of the reactive power outer loop will cause the characteristic of the synchronous condenser at the low frequency to transform into that of the current source, reducing the voltage-supporting capability of the system during the dynamic regulation process after fault clearance. Next, a short-circuit ratio index for the entire process of a renewable energy station with synchronous condensers is proposed, whose physical meaning is voltage sensitivity. This index can effectively describe the influence of different control strategies on voltage stability at multiple time scales, which explains the adaptability of using subtransient reactance parameters of the synchronous condenser to calculate operation limits. Finally, the correctness of the analysis conclusions is verified through RTDS hardware-in-the-loop simulation.
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LUO Shujie, CHEN Lei, ZHAO Boyuan, MIN Yong
2025,49(11):168-177, DOI: 10.7500/AEPS20240508005
Abstract:With the continuous increase in the proportion of power electronic equipment such as new energy resource generation converters, the strength of the power grid is getting weaker. It is very easy to generate transient overvoltage after AC short-circuit fault, which triggers cascading failures. Based on the low voltage ride-through (LVRT) control model of voltage source converter (VSC), firstly, this paper analyzes the generation mechanism of transient overvoltage and the influence of phase-locked loop (PLL) dynamics on transient overvoltage. Secondly, based on the PLL control equation, the approximate analytical solution of the phase for the three-stage PLL is solved, and the transient overvoltage analytical formula is derived from the voltage phasor diagram. Moreover, the transient overvoltage analytical equation is deduced from the voltage phase diagram. Then, the continuous variation trend of the influence of control parameters on the transient overvoltage level after fault clearing is obtained from the overvoltage analytical equation. Finally, MATLAB electromagnetic transient simulation is utilized to verify the validity of the derived analytical equation for the voltage at the grid-connected point and the conclusions related to the overvoltage mechanism influenced by parameters.
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WANG Shen, WEI Xingshen, ZHU Weiping, ZHU Daohua, GUAN Zhitao
2025,49(11):178-188, DOI: 10.7500/AEPS20240523001
Abstract:Log abnormity detection is a critical technology for monitoring the operation of distribution master station systems and identifying anomalous behaviors. Existing deep learning-based log abnormity detection methods rely on a large amount of labeled training data, yet the lack of annotated training data in power distribution master station systems leads to significant performance degradation in log abnormity detection. Based on the contextual reasoning performances of large language model (LLM), this paper proposes LogAdapt—a training-free log abnormity detection scheme for distribution master stations. The proposed in-context learning (ICL) example filtering algorithm is designed to dynamically select a number of high-quality ICL examples from a small amount of locally labeled online logs tailored to different types of logs. By integrating task descriptions and human expert knowledge, it automatically constructs text prompts to guide LLM in completing the task of log abnormity detection in distribution master station. The experimental results show that the proposed scheme has better performance compared to existing schemes.
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LI Xinran, TANG Aihong, ZHANG Kanjun, TENG Jie, WANG Wenhao, YANG Huiyuan
2025,49(11):189-198, DOI: 10.7500/AEPS20240605002
Abstract:Transition methods for equipment maintenance on the 220 kV side of traditional substations only consider the transmission grid reconfiguration, overlooking the mitigation effect of the 110 kV high-voltage distribution network reconfiguration on transmission congestion during the period of maintenance transitions. Therefore, an optimization method for maintenance decisions on 220 kV side of substations considering collaboration of transmission and distribution is proposed. Firstly, transitional measures for the transmission and distribution network under the maintenance mode are analyzed, and based on the topology of the high-voltage distribution network, a calculation model of power supply paths for regional high-voltage distribution network is extracted in the basic form of a connection matrix. Furthermore, a load transfer model based on optimal AC power flow is constructed to calculate the operational boundaries of the interface branches between the transmission and distribution networks. By defining composite minimum paths that satisfy both network structure conditions and operational boundary constraints, feasible topological states for the high-voltage distribution network are formed. Finally, optimal operation states for the high-voltage distribution network are sought based on topological performance indicators, and a comprehensive risk indicator is constructed to optimize the decision-making of transmission and distribution maintenance transition schemes. The effectiveness of the proposed method is verified through the analysis of the maintenance transition scheme on the 220 kV side of a substation in a local power grid system of China.
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XU Bin, LIU Ren, TANG Bo, WANG Shuai
2025,49(11):199-207, DOI: 10.7500/AEPS20240616001
Abstract:Accurate prediction of overhead line ampacity serves as the foundation for achieving dynamic line rating. Conventional single-point prediction models face challenges in effectively capturing the volatility and randomness of meteorological factors influencing ampacity (e.g., wind speed and ambient temperature), while ampacity prediction errors exhibit characteristics of time-varying correlations and conditional distribution. To address these limitations, this paper proposes a novel framework employing serial ensemble learning-based Bagging integrated with parallel ensemble learning methods (AdaBoost, XGBoost, and LightGBM) to develop an ampacity point prediction model. This hybrid approach overcomes the constraint of limited generalization capability of single model while enhancing prediction accuracy and stability. Through analysis of measured data, this paper demonstrates the time-varying correlations and conditional distribution characteristics in ampacity prediction errors. Subsequently, this paper introduces Sklar's theorem and dynamic Copula functions to establish a conditional distribution model for ampacity prediction errors. By integrating the proposed point prediction model with the conditional distribution model, this paper develops a probabilistic ampacity prediction method that integrates serial-parallel ensemble learning and multi-error characteristic fusion. Compared with traditional methods, the conservativeness indicator of the proposed method is reduced by 28.69%.
Volume 49,2025 Issue 11
>Review·Perspective
>Basic Research
>Application Research
Special Issue
Virtual Special Issue
- Views
- Hot Topics
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CHEN Chun, ZHAN Luxin, CAO Bozhong, CAO Yijia, LI Yong, LIU Junle
Available online:June 13, 2025 DOI: 10.7500/AEPS20240723005
Abstract:The increasing integration of power electronic devices in distribution networks has exacerbated harmonic currents, challenging the reliability of traditional transformer differential protection reliant on second harmonic restraint. Additionally, the single-characteristic identification methods are affected by the distributed generator (DG) types and closing angles, and cannot accurately distinguish fault currents and inrush currents in different scenarios. In order to improve the identification accuracy of excitation inrush current, this paper proposes a multi-perspective time-frequency analysis method that comprehensively integrates time-domain, frequency-domain, and time-frequency-domain characteristics. Bayes algorithm is used to optimize the classification parameters of eXtreme Gradient Boosting (XGBoost), improve the generalization ability of the model, and achieve accurate identification of fault current and excitation inrush current under different capacities and types of DG connected. The SHapley Additive exPlanations (SHAP) value analysis method is used to reveal the contribution of each characteristic value in the identification model. Based on simulation and on-site measurement data, the proposed excitation inrush current identification method is validated, and the accuracy of identification for sample data is close to 100%.
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ZHANG Yujia, YUAN Ye, ZHOU Suyang, ZHU Hong, ZHOU Aihua, CHEN Qingquan
Available online:June 09, 2025 DOI: 10.7500/AEPS20240112003
Abstract:With the rapid expansion of distribution network scale and the high penetration of distributed resources, the increasing complexity of distribution network topology poses significant challenges to fault location analysis. Conventional matrix-based algorithms and intelligent optimization algorithms require dynamic reconstruction of network matrices or optimization models based on evolving topological information, leading to excessive computational burden and complexity, as well as inefficient data processing and computation. This paper first constructs a graph data model for distribution network topology. Through graph projection techniques, it extracts an optimized subgraph tailored to fault tracing task scenarios from the panoramic power grid graph. Building on this, the Yen shortest path search algorithm is employed to identify potential fault paths from the power source to abnormal nodes. By traversing line nodes and analyzing current violation information, the fault section is accurately identified. The proposed method addresses the challenges of precise topological representation and rapid searching in power grids, enabling fast and accurate fault location for large-scale complex distribution networks. While ensuring fault back-tracing accuracy, this methold significantly enhances fault search efficiency.
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LIANG Hao, QIN Chuan, XIE Huan, LIANG Beihua, WU Tao, WANG Xuanyuan, WU Long
Available online:May 29, 2025 DOI: 10.7500/AEPS20240807002
Abstract:Deploying the distributed synchronous condenser in the renewable energy station is an effective measure to improve the voltage-supporting ability of the power supply side in the whole process. However, the existing cascade strategy of “reactive power outer loop + voltage inner loop” is adopted to integrate the synchronous condenser into the automatic voltage control system (AVC) of the station, which limits the low-frequency voltage source characteristics of the synchronous condenser. First, this paper describes the existing problems of the synchronous condenser integrated into AVC and proposes the requirement for constant voltage for the synchronous condenser throughout the whole process. Based on the topology of the renewable energy station with distributed synchronous condensers, the reactive voltage conversion coefficient and the reactive power shunt influence factor are analyzed. Then, this paper proposes a new scheme of “constant voltage + reactive power shunt suppression” for integrating the synchronous condenser into AVC. The program is developed on an equipment of domestic mainstream manufacturer, and the effectiveness of the proposed scheme is verified by the hardware-in-the-loop simulation of the dual controllers of synchronous condenser excitation and AVC at the station. Finally, the engineering application has been completed in an actual renewable energy station. The field operation results show that the proposed scheme realizes the steady-state regulation of multi-type reactive power equipment of the station, effectively reduces the voltage fluctuation amplitude at the grid-connection point of the station, fully utilizes the voltage source characteristics of the synchronous condenser in the whole process, and guarantees the voltage stability margin of the system.
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LIU Yuliang, LI Yaowang, DU Ershun, ZHANG Ning, KANG Chongqing, DU Siyuan
Available online:April 27, 2025 DOI: 10.7500/AEPS20240905004
Abstract:Energy activities are the primary source of carbon dioxide emissions. Accurate and near real-time monitoring of carbon emissions from energy activities is fundamental for governments and enterprises to grasp the total carbon emissions and formulate carbon management strategies. However, existing accounting methods are insufficient for high-frequency carbon emission calculations, whereas direct measurement methods necessitate additional equipment to be installed. Consequently, low-cost and high-frequency carbon emission estimation techniques remain unavailable. To address this issue, by using the correlation between electricity consumption and energy activities, this paper proposes an estimation technology for carbon emissions from energy activities based on power big data (abbreviated as “electricity-based carbon emission estimation” technology). Firstly, the methodological framework for “electricity-based carbon emission estimation” technology is proposed. From a data-centric perspective, a detailed investigation and analysis on related foundational data are conducted, and the potential application scope of existing data is explored. Secondly, a time-series based “electricity-based carbon emission estimation” model applicable at regional, industrial, and enterprise levels is constructed, followed by an evaluation of its effectiveness using actual data from China. The analysis results demonstrate that the proposed method can significantly improve the timeliness of carbon emission estimation. Finally, the future research and potential application directions for the “electricity-based carbon emission estimation” technology are prospected.
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LI Bo, WEI Guangrui, ZHONG Haiwang, LIU Hui
Available online:April 15, 2025 DOI: 10.7500/AEPS20240409003
Abstract:The IEEE test case has been widely used for simulation testing in various fields such as power system planning and operation. However, due to data privacy concerns, it isn"t easy to access publicly available datasets of actual power system generation and network structures. To address the issue, based on the evolution concept of three-generation power grids, a new transmission test system generation method is proposed to build test cases that reflect actual power system network structures. Firstly, a transmission expansion planning model considering N-1 security constraints is established. Secondly, the optimization objectives and constraints are proposed according to the characteristics of different stages of network development to simulate the evolution process of the electricity network. To enhance the solving efficiency of the model, a binary representation method of transmission corridors is introduced to reduce the number of 0-1 variables. Finally, the provincial power grid is used as an example to verify the effectiveness of the proposed open-source power system dataset, according to the statistical characteristics of complex networks. The proposed dataset is also applied to optimal transmission switching for further verification.
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Wang Shen, WEI Xingshen, ZHU Weiping, ZHU Daohua, GUAN Zhitao
Available online:February 27, 2025 DOI: 10.7500/AEPS20240523001
Abstract:Log anomaly detection is one of the key technologies to monitor the operation of distribution master station system and identify abnormal behavior. Existing log anomaly detection methods based on deep learning rely on a large amount of in-domain training data, and the scarcity of training data will lead to a significant decline in performance. Aiming at the above problems, based on the contextual reasoning characteristics of large language models, an adaptive hint strategy is designed and a training-free anomaly detection scheme for distribution master logs is implemented. Firstly, a demonstration example filtering algorithm is designed to dynamically select several high-quality demonstration examples from a small number of labeled local logs for different online logs. Then, combined with the task description and human experience knowledge, a text hint is automatically constructed to guide the large language model to complete the anomaly detection task of distribution master station logs. The experimental results on the general data set and the self-built distribution master station data set show that the proposed scheme has better performance than the existing methods, showing higher flexibility and generalization.
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WANG Ziyuan, XU Yin, WU Xiangyu, LI Jiaxu
Available online:February 26, 2025 DOI: 10.7500/AEPS20241024004
Abstract:The proportion of electricity received outside the urban power grid is high, and extreme events leading to connectivity failures in the power transmission channels of the urban superior power grid may cause major power outages. In this extreme scenario, by flexible self-configuration operation and supply guarantee of microgrid clusters, the critical load survival can be achieved. However, the large transient frequency fluctuation of microgrids during sudden power shortages directly affects the success or failure of self-configuration operation and supply guarantee. Firstly, a framework for solving the extreme survival problem of microgrid clusters is proposed, and dynamic frequency constraints for microgrids considering multi-source collaboration are constructed based on the system frequency response model. Secondly, the frequency response model of microgrid containing nonlinear constraints such as ordinary differential equations and limiting links is differentially discretized based on the forward Euler method. Then, according to the generator tripping and load shedding during the transient frequency response of microgrid clusters, a microgrid cluster model for extreme survival is constructed considering inertia equivalent constraints, control action delay constraints and dynamic segmentation constraints. By solving a mixed-integer linear programming model, the emergency frequency control strategy and the segmentation state of microgrid clusters are obtained to coordinate multiple frequency regulation resources and minimize the load shedding volume while ensuring frequency safety. This model can be efficiently solved using mature commercial solvers. Finally, the effectiveness and superiority of the proposed emergency frequency control strategy of microgrid clusters are verified through numerical simulation analysis.
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JIAO Zhijie, XU Yin, LIU Zhao, WANG Xiaojun, HE Jinghan, SI Fangyuan
Available online:December 30, 2024 DOI: 10.7500/AEPS20240519001
Abstract:The arrival of a cold wave triggers a sudden drop in weather temperature, at this time, the energy consumption of the load increases, the output of renewable energy sharply decreases, the system backup and the power supply capacity of the higher-level power grid are insufficient, resulting in a significant power shortage problem within the power grid in a short period of time. With the increasing number of electric vehicles on the load side and the improvement in the responsiveness of flexible resources, the load-side flexible resources are adjusted to compensate compensate for the power shortage caused by the cold wave. This paper first clarifies that the flexibility of resources varies with changing scenarios, explores and constructs models of flexible resources such as electric vehicles during cold wave events. Secondly, considering that electric vehicles in flexible resources need to participate in grid scheduling through aggregation, a method for aggregating electric vehicles with uncertain connection times is developed. Subsequently, based on the flexible resources during cold wave events, non-residential loads, and residential loads, a rolling optimal scheduling method for the power grid during cold wave weather is proposed with the objective of minimizing social losses. The electricity adjustment of various resources in the grid during cold wave weather is determined. Finally, through case studies, the proposed method is shown to effectively address power shortages in the grid during cold wave events.
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WANG Pengwei, XU Bingyin, LIANG Dong, WANG Lianhui, WANG Chao, ZOU Guofeng
Available online:October 31, 2024 DOI: 10.7500/AEPS20240315002
Abstract:Distinguishing whether faults in medium voltage distribution lines are caused by lines touching trees is of great significance for clarifying the causes of forest fires and preventing line faults from causing forest fires. The zero-sequence currents of various high-impedance grounding faults are obtained through prototype experiments in the paper, and the long-term variation features of the zero-sequence current waveforms of high-impedance grounding faults are analyzed. Analysis shows that there are significant differences in the fluctuation, monotonicity, and sharpness of the waveforms of the effective value of the zero-sequence currents for line touching trees grounding faults compared to other high-impedance grounding faults. A multi feature fusion parameter set including standard deviation, discrete coefficient, kurtosis, skewness of the zero-sequence current effective value curve is designed, and a ientification method for tree-touching grounding fault of medium-voltage line based on support vector machine is constructed. The results showed that the proposed method achieved a fault recognition accuracy of 98%.
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LIU Jie, SHI Fang, SONG Xuemeng, TIAN Shuoshuo, NIE Liqiang
Available online:October 17, 2024 DOI: 10.7500/AEPS20231101004
Abstract:The existing intelligent assessment methods for transient frequency in power systems do not adequately consider the temporal characteristics of input data. Therefore, a frequency safety assessment method for power systems based on intelligent prediction of transient frequency response curves is proposed in the paper. A multivariate sample convolutional interactive network is designed to fully exploit the temporal characteristics of power system measurement data, thereby improving the prediction accuracy of power system transient frequency response curves; Key indicators such as maximum frequency deviation, occurrence time of maximum frequency deviation, and the metastability frequency are calculated based on the predicted frequency response curves, and the frequency safety of the system is comprehensively assessed. Simulation tests are conducted on frequency stability standard examples, and the results showed that the proposed method effectively improved the accuracies of frequency response curve prediction and system frequency safety assessment compared to classical methods such as deep learning.
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LI Yong, LI Yinhong, LIU Huanzhang, LIU Yang
Available online:October 09, 2024 DOI: 10.7500/AEPS20240228008
Abstract:The last section of zero-sequence current protection of AC line adopts 300 A, which has the risk of disordered tripping.Therefore, a new principle of high-resistance grounding distance relay based on zero-sequence reactance line and non-fault phase polarization is proposed. The relay adopts the technical route of phase selection before measurement. The phase selection element combines zero-sequence reactance line and non-fault phase polarization method to form a variety of combined criteria to complete the phase selection. Due to the phase difference between the zero-sequence current at the protection installation and the zero-sequence current at the fault point, the zero-sequence reactance lines of the single-phase grounding fault phase and the advance phase of the inter-phase grounding fault have aliasing region when the fault point is near the setting point. The large variation of the operation voltage of the non-fault phase is not conducive to distinguishing the two types of faults in the aliasing region, and thus the phase selection element is divided into low-resistance module and high-resistance module. The low-resistance module adopts the zero-sequence reactance line with the downward bias, which is used to identify the near end and low-resistance short circuit. With the assistance of the low-resistance module, the high-resistance module only needs to deal with the faults near the set point, which reduces the difficulty of distinguishing the two types of faults . After phase selection, the operation voltage before fault is obtained by non-fault phase polarization method, so as to determine the operation characteristics of the relay. The ability of high-resistance distance relay to withstand the transition resistance is far beyond the requirements of the regulations, which improves the selectivity of ground backup protection to high-resistance faults.
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HAN Zhaoru, SHI Fang, ZHANG Hengxu, JIN Zongshuai, YUN Zhihao
Available online:September 29, 2024 DOI: 10.7500/AEPS20240116008
Abstract:The accurate and reliable detection of high-impedance grounding fault (HIGF) is a challenging issue in fault handling for distribution networks, and normal capacitor switching operations can cause interference. Addressing this problem, a disturbance-resistant detection method for HIGFs based on zero-sequence Lissajous curve analysis is proposed in this paper. Firstly, the zero-sequence electrical quantities of HIGFs and capacitor switching disturbances are theoretically derived. There is no regular difference in the traditional time-frequency domain feature aspect between the two, thereby clarifying the cause of the interference. Further, the zero-sequence current and voltage waveforms are reconstructed into zero-sequence Lissajous curves. A quantitative index for the distortion complexity of the Lissajous curve trajectory shape based on the mathematical morphology theory is proposed, and an adaptive starting criterion is designed in combination with the probability distribution law of the zero-sequence Lissajous curve area. The disturbance-resistant detection algorithm for high-impedance grounding faults in the noise scenario is presented. Finally, the effectiveness and reliability of the proposed method are verified through electromagnetic transient simulation examples and real fault tests in the distribution network.
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HE Zhiyuan, GAO Chong, YE Hongbo, YANG Jun, WANG Chenghao, SHENG Caiwang
Available online:August 29, 2024 DOI: 10.7500/AEPS20240506007
Abstract:Controllable line-commutated converter(CLCC) is a new type of DC converter equipment proposed to solve the commutation failure problem of conventional DC transmission converter. In June 2023, the world"s first set of controllable phase converter valves for the ±500kV/1200MW Gezhouba to Shanghai Nanqiao high-voltage DC transmission system renovation project (hereinafter referred to as "Genan renovation project") was successfully put into operation. The technical requirements and principles of CLCC are analyzed, as well as its technical and economic benefits. Combined with the input conditions of the Genan renovation project system, the electrical parameters and structural design scheme of the controllable line-commutated converter valve were proposed, and the equipment development was completed. The type test scheme and test parameters were proposed, and the type test assessment was completed. According to the technical characteristics of the controllable line-commutated converter valve, field tests such as low-voltage test, open line test, and artificial short-circuit test were carried out to ensure the smooth operation of the project. The operation performance since the project put into operation was introduced, and the correctness of the control sequence during the AC fault was analyzed based on the field recording. Finally, the application prospects of the controllable? line-commutated converter valve in UHVDC projects, provincial AC liaison line renovation and other scenarios were prospected and analyzed, providing a reference for the further promotion and application of the controllable commutation converter valve.
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ZHAO Ziyu, CHEN Yuanrui, CHEN Tingwei, LIU Junfeng, ZENG Jun
Available online:April 28, 2024 DOI: 10.7500/AEPS20230914003
Abstract:A regional-level ultra-short-term load forecasting model based on a spatio-temporal graph attention network is proposed in this paper. Firstly, based on the existing regional-level load, cell partitioning is carried out to construct a graph topology that considers cell correlation. Secondly, effective features are extracted from the spatial, feature, and temporal dimensions through the graph attention network, one dimensional convolutional network and gated recurrent unit, connecting the fully connected layers to output the results. Finally, simulation validation is conducted based on real power load data from the New England region of the United States, and model attention weights are extracted to analyze spatial dependencies between cells. The results show that, compared with traditional models, the proposed model provides higher accuracy and stability with different prediction steps, effectively exploiting the spatial dependence of regional spatial load.
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XIE Longtao, XIE Shiwei, CHEN Kaiyue, ZHANG Yachao, CHEN Zhidong
Available online:January 04, 2024 DOI: 10.7500/AEPS20230628010
Abstract:With the large-scale development of electric vehicles, it is of great significance to study how to effectively consider the travel behavior mechanism of users and formulate rational charging prices for charging stations for the collaborative optimization and scheduling of power-transportation networks. To solve this problem, this paper proposes a pricing strategy for charging stations in the power-transportation coupling network considering the user travel cost budget. Firstly, a transportation user equilibrium model considering the travel cost budget is established, and the equilibrium state is equivalently described through variational inequalities, so as to characterize the travel demands and charging behaviors of electric vehicles. Secondly, a second-order cone optimization model for distribution networks considering power reduction is constructed. The charging station pricing problem has been transformed into an optimization problem with variational inequality constraints, and an alternating iteration algorithm combined with an extra-gradient algorithm is designed to solve the problem. Finally, the effectiveness of the proposed model and methods is verified through a case, and the results show the necessity of considering the travel cost budget for charging pricing in coupled networks.
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LI Xiang, Liu Yuhang, ZHANG Qi, WU Xin
Available online:September 12, 2023 DOI: 10.7500/AEPS20230425001
Abstract:The illegal charging behavior of electric bicycles (EBs) in households has temporal randomness and spatial concealment,which poses significant safety hazards and is difficult to effectively manage. A non-intrusive real-time monitoring system for EB charging based on wavelet detection and feature graph decision is proposed, utilizing the characteristics of real-time autonomous execution and easy promotion of non-invasive monitoring systems. Considering the physical structure and charging characteristics of EB loads, the typical common characteristics of EB loads are analyzed from both transient and steady-state perspectives. The EB proprietary feature map with strong distinguishability and universality is constructed in advance to realize consistent and structured expression of EB steady-state common features. In the actual monitoring process, in order to reduce the computational power demand and data transmission pressure of the system, EB specific transient phenomena with high-frequency components are accurately located based on wavelet transform to complete EB like event detection. Finally, extract event waveforms and train efficient classifiers through graphs for load identification and real-time upload. By monitoring actual users, the effectiveness of the monitoring system has been verified, which can effectively solve the problem of charging EBs in buildings and households.
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ZHU Jiebei, LIU Donghao, LI Bingsen, YU Lujie, TIAN Na, JIA Hongjie
Available online:June 26, 2023 DOI: 10.7500/AEPS20220905004
Abstract:To reduce energy consumptions of data centers, the accurate establishment of data center server power consumption model is crucial. The traditional server power consumption model, which is only based on CPU utilization, ignores the delayed dynamic characteristics of server power changes influenced by temperature variables, and cannot distinguish the effect of CPU operating statuses based power consumption layering (CPL) under different CPU operating statuses, resulting in large modeling errors. To this end, a server power consumption model based on temperature estimating by CPU operating statuses (PMTC) is proposed in this paper. By taking the temperature variables into account in the model building stage, PMTC captures the delayed dynamic characteristics of server power consumption changes influenced by the delayed change of the CPU core temperature, eliminating the modeling error caused by the CPL effect. In the power calculating stage, PMTC identifies specific CPU operating statues, and then precisely estimates the CPU core temperature, avoiding extra measurement on temperature variables. A server power consumption test platform is established to compare the proposed PMTC and traditional server power consumption models, verifying that PMTC can restore the delayed dynamic characteristics of server power consumption change without increasing input date dimension and reduce the model calculation error.
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ZHENG Yao, ZHANG Jie, YAO Wenxuan, QIU Wei, TANG Sihao
Available online:March 16, 2023 DOI: 10.7500/AEPS20220813001
Abstract:As the power system gradually moves toward a new ecosystem of energy interconnection and the deep coupling of the network layer and physical layer, the threat of network attacks on the power system continues to rise. The source identity (ID) spoofing attack, as a new and complex, strong stealthy false data injection attack, can cause the grid control system to misjudge and cause system paralysis. To address this problem, a spatial feature-based method is proposed for detecting false data injection attacks on synchronized measurements of power grids. It has extracted different spatial features of the synchronized measurement devices at different locations by variational modal decomposition (VMD) and improved discrete orthonormal Stockwell transform (IDOST), so as to extract the authentication information of the measurement data without losing the spatial features of the measurements. Combined with light convolutional neural network (LCNN) to evaluate the likelihood of measurement data being attacked by source ID to enhance the speed of detection response. The effectiveness of the method is verified by the detection results of actual multi-point synchronous measurement data.
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XIAO Bai, ZHANG Bo, WANG Xinwei, GAO Ningyuan
Available online:February 20, 2023 DOI: 10.7500/AEPS20220807002
Abstract:Wind power prediction is very important for the economic dispatch of power systems containing wind power. Aiming at the problem that point prediction is difficult to describe the uncertainty of wind power, a short-term wind power interval prediction method based on combined mode decomposition and deep learning is proposed. Firstly, the original wind power sequence is decomposed into multiple modal components by using the improved complete ensemble empirical mode decomposition with adaptive noise, and the high-frequency strong non-stationary components are decomposed again by using the variational mode decomposition. On this basis, the sample entropy is used to calculate the complexity of each component and reconstruct them into trend components, oscillation components and random components. Then, the three components are input into the Bayesian optimization bidirectional long short-term memory neural network to establish their respective prediction models, and the point prediction values of the three components are obtained. The mixed kernel density estimation method is used to estimate the error distribution of the prediction results of oscillation components and random components, and the overall interval prediction results are obtained by combining the point prediction values. Finally, the actual examples show that this method has higher prediction accuracy than other models.
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LIU Wensong, HU Zhuqing, ZHANG Jinhui, LIU Xuejing, LIN Feng, YU Jun
Available online:September 27, 2022 DOI: 10.7500/AEPS20210323003
Abstract:Considering the characteristics of small scale, nested entities and abbreviated entities for electric corpus, the named entity recognition (NER) based on enhanced vectors of text features is proposed. Firstly, by the way of the low grain word segment and the preset dictionary, the semantic information in Chinese words is properly utilized, and the transmission errors caused by word segment are decreased. Secondly, the features of inner structure of a single Chinese word is learned by the word-level bidirectional gated recurrent unit (Word BiGRU). Together with the features of the part of speech for words and word length, the enhanced word vector is built by concatenating these features vectors with word vectors. Finally, the NER model is designed with BiGRU, attention mechanism and conditional random field (CRF). The proposed method is verified using electric corpus and F1 measurement reaches 87.02%, which proves the effectiveness of NER for electric power industry.
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ZHANG Yi, YAO Wenxu, SHAO Zhenguo, ZHANG Liangyu
Available online:August 18, 2022 DOI: 10.7500/AEPS20211203007
Abstract:Aiming at the problems of abnormal operation condition monitoring for environmental protection in polluting enterprises at present, such as difficult implementation, large identification errors and easy tampering with the results, this paper proposes an identification method of abnormal operation conditions for environmental protection based on power quality monitoring data. The multi-dimensional power quality data obtained from non-invasive load monitoring at the public power entrance of enterprise equipment are used to train the model of condition classification, to realize abnormal condition identification, which is different from the existing scheme of power consumption monitoring with a separate meter installed for each device. First, the time series change-point detection and the clustering calculation for the characteristic data representing the production conditions are carried out to realize the division of production operation conditions of enterprises. Then, combined with the operation of environmental protection equipment, the categories of environmental protection operation conditions for classification are obtained. Furthermore, the operation condition scenarios related to environmental protection are classified and learned by the Stacking learning model. Finally, the trained classification model is used to identify the abnormal operation conditions for environmental protection in the enterprise. The effectiveness of the proposed method is verified by the simulation test data and the actual enterprise data.
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YANG Daye, SONG Ruihua, XIANG Zutao, LIU Dong, CHAO Wujie, YAN Yuesheng
Available online:May 26, 2022 DOI: 10.7500/AEPS20220126001
Abstract:Since the capacitance per unit length of cable is more than 20 times that of overhead lines with the same voltage level, more and more offshore wind power is connected to the grid through AC cable, which reduces the natural resonant frequency of the transmission system and increases the resonant risk of the system. Aiming at the resonant overvoltage phenomenon in the process of grid connection of an offshore wind farm, the fault recording data are analyzed. Based on the impedance model of the transmission system, the mechanism is analyzed, and through the electromagnetic transient simulation, it is verified that the natural resonant frequency near the double frequency of the transmission system is the fundamental reason for the resonant overvoltage caused by the operation of no-load transformer and no-load line in the wind farm. Combined with the engineering practice, the joint suppression measures of changing the operation mode of the transmission system, optimizing the control and protection system of the static var generator and increasing the access load are proposed. Finally, the effectiveness of the proposed measures is verified by simulation and field tests.
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JIANG Wei, WANG Minghua, CHEN Jinming, LIU Jiangdong, PU Shi, XU Zhiqi
Available online:May 13, 2022 DOI: 10.7500/AEPS20211031001
Abstract:In order to improve the efficiency of power supply reliability calculation for the complex distribution system, a reliability calculation method based on the Neo4j graph database is proposed. Firstly, the topology structure of the distribution network is stored in vertex-edge form through the graph database. Meanwhile, the feeder classification and load partitioning in the complex distribution system is completed by using the characteristics of different types of edges in the Neo4j graph database, and the distribution network diagram model based on the graph database is built. Secondly, the subgraph division of the distribution network diagram model is combined with path search, and the model simplification is completed based on each subgraph. Finally, the power supply reliability analysis of the distribution system is realized based on the minimal path reliability algorithm combined with the efficient shortest path query and other functions of the Neo4j graph database. The effectiveness of the proposed method is verified by comparing the Roy Billinton test system and an actual 10 kV distribution network in China for algorithm verification.
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LI Zheng, CHEN Wu, HOU Kai, SHI Mingming, MOU Xiaochun, ZHU Jinsong
Available online:April 26, 2022 DOI: 10.7500/AEPS20210806005
Abstract:When the flexible ring network controller cancels the interface transformer, the transmission of the zero-sequence voltage component cannot be prevented when the AC side fails, thus increasing the fault range. Therefore, this paper uses classical circuit analysis and positive and negative sequence analysis algorithms to explain the basic principles of the formation and transmission of zero-sequence voltage components. A topology of the flexible ring network controller without the interface transformer is proposed. The AC side converters are all modular multilevel converters with traditional half-bridge sub-modules. And the full-bridge sub-module valve strings are connected in series on the positive and negative polarity busbars. Utilizing the ability of the full-bridge sub-module to output positive and negative voltages, the DC side voltage fluctuations are suppressed, and the fault range is prevented from expanding. By using the MATLAB/Simulink software, the characteristics of zero-sequence voltage suppressed during the fault are simulated and analyzed. The simulation results verify the correctness of the theoretical analysis and the effectiveness of the proposed topology.
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YOU Wenxia, LI Qingqing, YANG Nan, SHEN Kun, LI Wenwu, WU Zeli
Available online:March 30, 2022 DOI: 10.7500/AEPS20210731001
Abstract:Aiming at the problems that the consumer power consumption data categories are unbalanced in electricity theft detection, and the ensemble learning method using voting as a combination strategy can not give full play to the advantages of multiple different learners, a model using Stacking ensemble learning to fuse multiple different learners is proposed and applied to electricity theft detection. Firstly, starting from the factors affecting electricity metering, six electricity theft behavior modes are simulated according to five common electricity theft methods; Secondly, synthetic minority oversampling technique (SMOTE) is used to process the unbalanced power consumption data, and k-fold cross-validation method is used to divide the balanced training sets to alleviate the overfitting caused by repeated learning; Then, the evaluation indicators and diversity metrics are employed to optimize different primary learners and meta-learners of the model, and a Stacking combination learning electricity theft detection model integrating the advantages and differences of different learners is constructed; Finally, the comparative analysis results of examples show that the proposed electricity theft detection model can effectively solve the imbalance of power consumption data categories, give full play to the advantages of different learners, and the evaluation index is good.
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Available online:December 01, 2021 DOI: 10.7500/AEPS20210510001
Abstract:While the flexibility of power systems operation can be improved by topology optimization, the dimension of system-level discrete decision variables, including the connections of lines and substation busbars, is prohibitively high. Thus, the topology optimization problem of power systems can hardly be solved by the conventional mixed-integer optimization method. Aiming at this problem, a reinforcement learning based method is proposed combining asynchronous advantage actor-critic and power system domain knowledge, which transfers the computational burden of online optimization to the offline agent training stage. The defined reward function is adopted to minimize the violations of power transmission line flow limits. Forced constraints verification is employed to reduce the searching space and improve the efficiency of the reinforcement learning. The fast computation of the topological structure optimization of power system operation is realized,and the operation security of power systems is enhanced. The effectiveness of the proposed method is validated by simulation testing results.
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ZHANG Liudong, ZHANG Haibo, XIONG Hao, LI Yanliu, PENG Zhiqiang, LI Feng
Abstract:In order to solve the different requirements of provincial and localized new energy control scenarios, such as peak regulation, active power control of transmission lines and transformer overload risk control, the active power cooperative control system architecture and strategy of provincial and localized new energy is studied. Using the provincial and localized coordination and centralized distribution collaborative technical routes, the automatic power generation control module of new energy is built in the provincial and localized dispatching master stations, and the "1+N+X" active power collaborative control system of full voltage level new energy is presented. Based on the system, two types of bidirectional collaborative support strategies for provincial and localized new energy are further proposed, namely, the localized dispatching master station will aggregate the adjustable resources under its jurisdiction to the provincial dispatching master station to meet the regulation requirements of provincial dispatching master station, and the localized dispatching master station can also generate adjustment instructions for emergency situations such as overload of device N-1, which will be issued to the resources of provincial dispatching master station for implementation after unified decision making, and comprehensively improve the power grid balance adjustment ability. The effectiveness of the proposed system architecture and strategy is verified through the actual operation effect of a provincial and localized dispatching, so as to ensure the security and stability of the power grid operation and maximize the new energy accommodation.
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JIN-Yangxin, XU Yongjin, HU Shuhong
Available online: DOI: 10.7500/AEPS20241117002
Abstract:As an important data source of low-voltage distribution grid， the measurement anomaly control of smart energy meter concerns electricity trading fairness. A line loss recognition model of weakly correlated measurement anomaly was devised, whose inputs are frozen active power and voltage recorded by energy meter, as well as partial archival information. The principals of the model is a residual neural networks with variable weights, which is utilized to forecast the measurement anomaly index. Aimed at the accuracy degradation owing to insufficient inputs, the network weights variability algorithm was proposed, which modifies the tradition that weights are fixed after training, whereas regards them as superposition of several eigenstate weights. Via the node correlation state that is not able to be input directly, the superimposing parameters of eigenstate weights were calculated in feed-forward operations, with model recognition performance improved significantly. Finally the validity of residual neural networks with variable weights was verified with the enhanced training and validation datasets; furthermore, the model was applied to the control of high-loss range in a city in Zhejiang to confirm its detection effect under actual conditions.
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CHEN Zhilin, LIU Hao, BI Tianshu
Available online: DOI: 10.7500/AEPS20250110002
Abstract:To address the challenges posed by the integration of a large number of distributed renewable energy resources, synchronized phasor measurement technology has been introduced into distribution networks, providing a more comprehensive data basis for system safety and stability analysis. To further enhance situational awareness in distribution networks, this paper proposes a disturbance identification method driven by synchronous measurement data. On one hand, a disturbance source identification method based on Granger causal analysis is introduced to determine whether the detected disturbance originates from the transmission side or the distribution side. On the other hand, an algorithm misjudgment disturbance identification method based on kernel support vector machines (SVM) is proposed to distinguish whether the disturbance is caused by errors in the instantaneous frequency estimation algorithm. This comprehensive approach ultimately enables the identification and extraction of genuine distribution network disturbances. The effectiveness of the proposed method is tested and validated using IEEE123 simulation data and field synchronous measurement data.
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Available online: DOI: 10.7500/AEPS20250204003
Abstract:Distributed travelling wave devices along the transmission lines need to be paired and can only perform multi-terminal fault location algorithm, which strictly relies on time clock synchronization. Single measuring point cannot independently identify fault segment or locate fault. In order to solve the drawbacks, the paper proposes a fault segment identification and fault location algorithm utilizing current travelling waves observed by single measurement along transmission lines. Firstly, by analyzing the the topological features of multiple lines connection at both ends of the transmission lines, it is found that the relative polarity determined by such topological type can be used to identify the properties of the subsequent current travelling waves at a single measuring point. Furthermore, the essential difference between the fault segment and the sound segment is found, that is, whether there is fault point reflection after the travelling wave enters the segment. Transmission line is divided into two segments of unequal length by using measuring point along the line, and the wave impedance at the measuring point is continuous. Wavefronts are identified according to the relative polarity and inherent arrival time differences of travelling waves in the segments, ultimately achieving fault segment identification and single-ended fault location. Massive data of actual measurement and simulation tests demonstrate the feasibility and effectiveness of the proposed algorithm. The method is not affected by different fault types, various fault resistances, and the deviations of travelling wave velocity and line length. The method exhibits no dead zones for the nearby faults. The installation positions of the traveling wave devices are versatile and flexible. The proposed method can serve as a backup algorithm and essential complement to existing double-ended fault location of distributed traveling wave devices. And it can be used as an independent fault segment indicator of transmission line.
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TIAN Xincui, CHEN Kaiwen, SHAN Jieshan, ZHANG Yining, YU Jinyun, LI Qiang
Available online: DOI: 10.7500/AEPS20240904005
Abstract:The fault information in grounding electrode lines is weak and well-hidden, making detection and fault location very difficult. Based on this, a new single-ended fault location algorithm for grounding electrode lines is proposed, utilizing broadband excitation injection and the short-time matrix pencil method (STMPM). First, a Gaussian signal excitation with an “oscillatory decay characteristic” is injected into the grounding electrode line in differential mode, ensuring that the injected excitation does not leak into the DC system side through the neutral bus and minimizing waveform distortion during the propagation of the signal along the grounding electrode line, thereby improving the detection efficiency of fault traveling waves. Secondly, sliding short-time windows are used to perform singular value decomposition (SVD) on the fault traveling waves. The eigenvalues obtained from the decomposition are used to distinguish between interference signals and fault signals, effectively amplifying the weak fault signals while suppressing the interference signals. Finally, the damping factor of the fault traveling wave within the short window is determined, establishing a one-to-one mapping relationship between the zero-crossing moment of the damping factor and the arrival time of the fault traveling wave, and the fault distance is then determined. Extensive simulations show that the distance measurement algorithm can effectively detect fault traveling waves and achieve high fault location accuracy.
