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模型—数据混合驱动的电网安全特征选择和知识发现:关键技术与工程应用
作者:
作者单位:

1. 清华大学电机工程与应用电子技术系, 北京市 100084;2. 电力系统及发电设备控制和仿真国家重点实验室, 清华大学, 北京市 100084;3. 即云天下(北京)数据科技有限公司, 北京市 100084;4. 广东电网有限责任公司电力调度控制中心, 广东省广州市 510600

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基金项目:

国家重点研发计划资助项目(2018YFB0904500);国家自然科学基金创新研究群体科学基金资助项目(51621065)


Hybrid Model and Data Driven Concepts for Power System Security Feature Selection and Knowledge Discovery: Key Technologies and Engineering Application
Author:
Affiliation:

1. Department of Electrical Engineering, Tsinghua University, Beijing 100084, China;2. State Key Laboratory of Control and Simulation of Power System and Generation Equipments, Tsinghua University, Beijing 100084, China;3. Jiyuntianxia(Beijing)Data Science Company, Beijing 100084, China;4. Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd., Guangzhou 510600, China

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    摘要:

    随着可再生能源的大规模并网、需求响应的逐步实现,电网运行方式的复杂性和波动性不断攀升,电力系统的安全运行正面临新的需求与挑战。因此,基于人工智能技术,在广东电网建立了“模型—数据混合驱动的电网安全特征选择和知识发现平台”,保证电网安全、稳定、经济运行。文中首先定义了电网安全特征和知识,阐述了模型—数据混合驱动的思想与具体实现方法,并分析了降低误差的手段;其次阐释了平台的并行计算技术;接着设计了平台的软硬件架构;最后,展示了平台在广东电网的实际应用效果,结果表明:(1)从运行规则制定层面,将运行专家离线制定粗放运行规则的模式,变革为人工智能在线发现精细运行规则的模式;(2)从运行规则应用层面,将调度员人工判定运行规则的模式,变革为人工智能实时判定运行规则的模式。

    Abstract:

    With the integration of large-scale renewable energy and the implementation of demand response, the power system operation scenarios have become increasingly complicated and variable, leading to new requirements and challenges in power system security operation. Therefore, based on artificial intelligences, a hybrid model and data driven platform for power system security feature selection and knowledge discovery is established in the Guangdong Power Grid in China, to keep power system in secure, stable and economic conditions. Firstly, the definitions of power system security feature and knowledge are put forward, the concepts for the hybrid model and data driven platform are described, and the methods for reducing error are analyzed. Secondly, parallel techniques for the platform are discussed. Thirdly, the software and hardware architecture of the platform is designed. Finally, the application results in the Guangdong Power Grid are demonstrated, which show that: (1)from the perspective of rule-making, the platform transfers the pattern that conservative operation rules should be made by expert offline to the pattern that specific operation rules can be discovered by artificial intelligences online; (2)from the perspective of rule-application, the platform transfers the pattern that operation rules used online should be determined by operators artificially to the pattern that the operation rules can be determined by artificial intelligences automatically.

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黄天恩,郭庆来,孙宏斌,等.模型—数据混合驱动的电网安全特征选择和知识发现:关键技术与工程应用[J].电力系统自动化,2019,43(1):95-101. DOI:10.7500/AEPS20180614002.
HUANG Tianen, GUO Qinglai, SUN Hongbin,et al.Hybrid Model and Data Driven Concepts for Power System Security Feature Selection and Knowledge Discovery: Key Technologies and Engineering Application[J].Automation of Electric Power Systems,2019,43(1):95-101. DOI:10.7500/AEPS20180614002.

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历史
  • 收稿日期:2018-06-14
  • 最后修改日期:2018-11-20
  • 录用日期:2018-10-19
  • 在线发布日期: 2018-11-20
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