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Adaptive Load Frequency Control of Wind Power System Based on Online Reinforcement Learning
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School of Electrical Engineering and Automation, Wuhan University, Wuhan430072, China

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This work is supported by National Key R&D Program of China (No. 2018AAA0101501).

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    Abstract:

    Large-scale wind power connected to the gird brings new uncertainties which affects the frequency response characteristics of the system. From the data-driven perspective, an online reinforcement learning method based on adaptive dynamic model for load frequency control is proposed. A low rank autoencoder feature extraction network is established to discover hidden features from the measured low-dimensional data. Based on the feature network, a sparse identification learning model for nonlinear dynamic system is established to detect the potential physical state of the dynamic model of the system and improve the online learning efficiency. Combined with model predictive control, real-time decision control is implemented. The proposed method can effectively solve the problem that traditional model predictive control depends on the accuracy of the system global model, enhance the adaptability of controller to the system dynamic model and effectively track the random fluctuation of wind power. Finally, the validity of the proposed control method is illustrated by load frequency control model integrated with type Ⅵ wind turbine.

    表 1 重构相平面平均DTW距离Table 1 Average DTW distance of reconfigurated phase space
    表 3 LFC物理模型参数学习结果Table 3 Learning results of parameters in LFC physical model
    表 5 Table 5
    图1 基于在线强化学习的LFC原理图Fig.1 Principle diagram of LFC based on online reinforcement learning
    图2 无LLAE的模型学习曲线Fig.2 Model learning curve without LLAE
    图3 有LLAE的模型学习曲线Fig.3 Model learning curve with LLAE
    图4 不同风电渗透率下系统频率响应Fig.4 System frequency responses with different penetration ratios of wind power
    图 风功率与预测值间偏差标幺值曲线Fig. Wind power deviation (pu)
    图 原系统庞加莱截面图Fig. Poincare Sections of original LFC system
    图 重构相平面庞加莱截面图Fig. Poincare Sections of embedded phase-space
    图 风功率与预测值间偏差标幺值曲线Fig. Wind power deviation (pu)
    图 原系统庞加莱截面图Fig. Poincare Sections of original LFC system
    图 重构相平面庞加莱截面图Fig. Poincare Sections of embedded phase-space
    图 样本效率Fig. Data efficiency
    图 样本效率Fig. Data efficiency
    表 4 Table 4
    表 2 LLAE隐藏层平均DTW距离Table 2 Average DTW distance of hidden layer in LLAE
    图 LFC仿真系统Fig. Diagram of single area power system
    图 自编码器原理图Fig. Schematic diagram of Autoencoder
    图 SINDy原理图Fig. Schematic diagram of SINDy
    图 模型预测控制原理图Fig. Schematic diagram of model predictive control
    图 基于强化学习的LFC控制流程图Fig. Flow chart of Model-based Reinforcement Learning for LFC
    图 LFC系统状态空间演变图Fig. evolution of LFC system over time
    图 LLAE隐藏层演变图Fig. evolution of hidden layer over time
    图 SINDy对LFC系统的离线训练效果Fig. SINDy prediction performance for LFC
    图 LLAE隐藏层演变图Fig. evolution of hidden layer over time
    图 SINDy对LFC系统的学习效果Fig. SINDy prediction performance for LFC
    图 在线强化学习效果Fig. Online reinforcement learning performance
    图 有、无模型参数误差系统频率响应Fig. Frequency responses for single area integrated wind power with or without model errors
    图 考虑模型误差风功率随机扰动下系统频率响应Fig. Frequency responses for single area integrated wind power with model errors
    图 有、无模型参数误差系统频率响应Fig. Frequency responses for single area integrated wind power with or without model errors
    图 考虑模型误差风功率随机扰动下系统频率响应Fig. Frequency responses for single area integrated wind power with model errors
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YANG Li,SUN Yuanzhang,XU Jian,et al.Adaptive Load Frequency Control of Wind Power System Based on Online Reinforcement Learning[J].Automation of Electric Power Systems,2020,44(12):74-83.DOI:10.7500/AEPS20190706006

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History
  • Received:July 06,2019
  • Revised:September 24,2019
  • Adopted:
  • Online: June 18,2020
  • Published: