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新一代人工智能技术在电力系统调度运行中的应用评述 被引量:83

Review on Application of New Generation Artificial Intelligence Technology in Power System Dispatching and Operation
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摘要 以深度学习、强化学习为代表的新一代人工智能技术及其应用是当前电力系统领域的研究热点。人工智能技术具有不依赖物理机理,计算速度快,辨别效率高等优点。但人工智能固有的可解释性差、稳定性弱等缺点也制约了其在电力系统一些场景的应用。文中梳理了新一代人工智能技术在电力系统负荷和新能源预测、故障诊断、在线稳定性评估、频率及电压优化控制和电网运行方式制定等调度运行场景中的应用,并进行了分析和评述。总结了现有研究中存在的问题,指出人工智能技术的应用应当以问题为导向,以场景为基础,以应用为目的。最后,对未来人工智能技术在电力系统调度运行中的应用作出了展望。 The new generation of artificial intelligence technology and its application represented by deep learning and reinforcement learning are the research hotspots in the field of power systems.Artificial intelligence technology has the advantages of independence of physical mechanism,high calculation speed and high discrimination efficiency.However,the inherent disadvantages of artificial intelligence,such as poor interpretability and weak stability,restrict its application in some scenarios of power systems.In this paper,the application of new generation artificial intelligence technology in power system load and renewable energy forecasting,fault diagnosis,on-line stability assessment,frequency and voltage optimal control and power grid operation mode formulation are summarized and analyzed.This paper summarizes the existing research problems and points out that the application of artificial intelligence technology should be problem-oriented,scenario-based and application-targeted.Finally,the future application of artificial intelligence technology in dispatching and operation of power system is prospected.
作者 赵晋泉 夏雪 徐春雷 胡伟 尚学伟 ZHAO Jinquan;XIA Xue;XU Chunlei;HU Wei;SHANG Xuewei(College of Energy and Electrical Engineering,Hohai University,Nanjing 210098,China;State Grid Jiangsu Electric Power Co.,Ltd.,Nanjing 210024,China;Beijing Kedong Electric Power Control System Co.,Ltd.,Beijing 100085,China)
出处 《电力系统自动化》 EI CSCD 北大核心 2020年第24期1-10,共10页 Automation of Electric Power Systems
基金 国家重点研发计划资助项目(2017YFB0902600) 国家电网公司科技项目(SGJS0000DKJS1700840)。
关键词 人工智能 电力系统 调度运行 深度学习 强化学习 场景适配 artificial intelligence power system dispatching and operation deep learning reinforcement learning scenario adaptation
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