1. 南瑞集团(国网电力科学研究院)有限公司, 江苏省南京市 211106; 2. 国电南瑞科技股份有限公司, 江苏省南京市 211106;3. 智能电网保护和运行控制国家重点实验室, 江苏省南京市 211106; 4. 国网江苏省电力有限公司, 江苏省南京市 210024
1. NARI Group Corporation(State Grid Electric Power Research Institute), Nanjing 211106, China;2. NARI Technology Co. Ltd., Nanjing 211106, China;3. State Key Laboratory of Smart Grid Protection and Control, Nanjing 211106, China;4. State Grid Jiangsu Electric Power Co. Ltd., Nanjing 210024, China
The rapid development of a new generation of artificial intelligence technology represented by deep learning, as a strategic technology that leads a new round of scientific and technological revolution and industrial transformation, has risen to a national strategy and has attracted the attention of all walks of life. As the “decision brain” of power system, regulation and operation of power grid is a comprehensive decision-making control combining a large amount of data, mechanism analysis, operation procedures and professional experience, which is very similar to the development of a new generation of artificial intelligence characterized by data-driven and knowledge-based guidance. Based on the analysis of the characteristics of the new generation of artificial intelligence technology, the business situation and requirements of the power grid regulation operation, the design idea, the overall architecture and main functions of the future artificial intelligence based dispatch control system are proposed. And the key technologies and potential application scenarios are analyzed from the aspects of high performance computing, regulation big data, power system prediction and identification based on deep learning, intelligent assistant decision based on knowledge graph and dispatch assistant based on voice interaction. Finally, the development and future of artificial intelligence in power grid regulation are summarized and forecasted.
SHAN Xin, LU Xiao, ZHAI Mingyu,et al.Analysis of Key Technologies for Artificial Intelligence Applied to Power Grid Dispatch and Control[J].Automation of Electric Power Systems,2019,43(1):49-57. DOI:10.7500/AEPS20180629002.