Application Analysis and Exploration of Hybrid-augmented Intelligence in Power Systems

被引：0
|
作者：
Fan S. [1 ]
Guo J. [1 ]
Ma S. [1 ]
Zhao Z. [1 ]
Wang T. [1 ]
机构：
[1] China Electric Power Research Institute, Haidian District, Beijing
来源：
Dianwang Jishu/Power System Technology | 2023年 / 47卷 / 10期
关键词：
artificial intelligence; human-in-the-loop; human-machine collaboration; hybrid-augmented intelligence; machine learning; power system;
D O I：
10.13335/j.1000-3673.pst.2023.0356
中图分类号：
学科分类号：
摘要：
The new generation of artificial intelligence (AI) technology will play an important role in promoting the digitalization, informatization and intelligence of the future power grids due to its high-dimensional state intelligent perception and rapid decision-making capabilities. However, its inherent shortcomings such as the poor interpretability and fragility also limit the further applications of the AI technology in power systems. This paper firstly introduces the hybrid-augmented intelligence (HAI) technology and its application development in the fields of vehicle autonomous driving and industrial robots. Combining the characteristics of the power system and the AI technology, the requirements of the power systems for the HAI are analyzed and summarized. Secondly, the key technologies involved in the human-machine collaborative HAI are analyzed in terms of the data processing, the model training and the model application. On this basis, the application of the HAI technology in the typical scenarios, such as the power system transient stability assessment and the grid section power flow regulation, is designed and analyzed, which provides a reference for the subsequent engineering applications. Finally, the challenges faced by the application of the HAI in power systems are analyzed and prospected, aiming to promote and enrich the development of the basic theories and the key technologies of the hybrid intelligence in power systems. © 2023 Power System Technology Press. All rights reserved.
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页码：4081 / 4091
页数：10
相关论文
共 41 条
  • [1] ZHANG Zhigang, KANG Chongqing, Challenges and prospects for constructing the new-type power system towards a carbon neutrality future, Proceedings of the CSEE, 42, 8, pp. 2806-2818, (2022)
  • [2] YANG Ting, ZHAO Liyuan, WANG Chengshan, Review on application of artificial intelligence in power system and integrated energy system, Automation of Electric Power Systems, 43, 1, pp. 2-14, (2019)
  • [3] SHAN Xin, LU Xiao, ZHAI Mingyu, Analysis of key technologies for artificial intelligence applied to power grid dispatch and control, Automation of Electric Power Systems, 43, 1, pp. 49-57, (2019)
  • [4] FAN Shixiong, LI Lixin, WANG Songyan, Et al., Application analysis and exploration of artificial intelligence technology in power grid dispatch and control, Power System Technology, 44, 2, pp. 401-411, (2020)
  • [5] ZHU Qiaomu, LI Hongyi, WANG Ziqi, Short-term wind power forecasting based on LSTM[J], Power System Technology, 41, 12, pp. 3797-3802, (2017)
  • [6] FAN Shixiong, LIU Xingwei, YU Yijun, Et al., Multi-source data and hybrid neural network based ultra-short-term bus load forecasting, Power System Technology, 45, 1, pp. 243-250, (2021)
  • [7] ZHOU Zihan, BU Guangquan, WANG Guozheng, Et al., Multi-agent complementary power system transient stability assessment based on data space reliability domain, Proceedings of the CSEE, 42, 15, pp. 5471-5483, (2022)
  • [8] LIU Zhenyao, GAO Fangyu, JIANG Haibo, Development and application of intelligent patrol-inspection robot system for transmission lines[J], Electric Power Information and Communication Technology, 17, 8, pp. 57-62, (2019)
  • [9] DU Yixing, HU Zhijian, WANG Fangzhou, Two-stage power system transient stability assessment based on post-disturbance measurement response information, Power System Technology, 46, 6, pp. 2230-2240, (2022)
  • [10] WANG Songyan, FAN Shixiong, CHEN Jianwen, Deep-learning based fault diagnosis using computer-visualised power flow, IET Generation, Transmission & Distribution, 12, 17, pp. 3985-3992, (2018)
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