A Research Review on Application of Artificial Intelligence in Power System Fault Analysis and Location

被引：0
|
作者：
He J. [1 ]
Luo G. [1 ]
Cheng M. [1 ]
Liu Y. [1 ]
Tan Y. [1 ]
Li M. [1 ]
机构：
[1] School of Electrical Engineering, Beijing Jiaotong University, Haidian District, Beijing
来源：
| 1600年 / Chinese Society for Electrical Engineering卷 / 40期
基金：
中国国家自然科学基金; 国家重点研发计划;
关键词：
Fault analysis and location; Fault identification; Fault location; Fault recognition; New generation artificial intelligence;
D O I：
10.13334/j.0258-8013.pcsee.200032
中图分类号：
学科分类号：
摘要：
With the development of the power grid, the fault analysis and location of power system has to face new challenges such as huge data, complicate fault characteristics, and difficult system design. The new generation of artificial intelligence (AI) which is good at feature learning, non-linear fitting and end-to-end modeling could provide alternative solutions for fault analysis and location. Based on the analysis of needs and rationalities of applying new generation AI in fault analysis and location, the application of new generation AI techniques was reviewed. Finally, their advantages and solutions for existing problems were discussed, and the possible development of new generation AI techniques in fault analysis and location was discussed. © 2020 Chin. Soc. for Elec. Eng.
引用
收藏
页码：5506 / 5515
页数：9
相关论文
共 48 条
  • [1] Liu Jiaying, Kong Xiangjie, Xia Feng, Et al., Artificial intelligence in the 21st century, IEEE Access, 6, pp. 34403-34421, (2018)
  • [2] Mircea E, Chen-Ching L, Abdel-Aty E., Artificial Intelligence and computational intelligence: a challenge for power system engineers, Advanced Solutions in Power Systems: HVDC, FACTS, and Artificial Intelligence, (2016)
  • [3] Luo Guomin, Yao Changyuan, Liu Yinglin, Et al., Stacked auto-encoder based fault location in VSC-HVDC, IEEE Access, 6, pp. 33216-33224, (2018)
  • [4] Bengio Y., Learning deep architectures for AI, Foundations and Trends® in Machine Learning, 2, 1, pp. 1-127, (2009)
  • [5] Wu Tong, Bajwa W U., Learning the nonlinear geometry of high-dimensional data: models and algorithms, IEEE Transactions on Signal Processing, 63, 23, pp. 6229-6244, (2015)
  • [6] Lecun Y, Bengio Y, Hinton G., Deep learning, Nature, 521, 7553, pp. 436-444, (2015)
  • [7] Yang Qingqing, Le Blond S, Aggarwal R, Et al., New ANN method for multi-terminal HVDC protection relaying, Electric Power Systems Research, 148, pp. 192-201, (2017)
  • [8] Xu Hongqiang, Structured design and application of power dispatching universal data object for dispatching and control cloud, Power System Technology, 42, 7, pp. 2248-2254, (2018)
  • [9] Wei Bo, Research on effects of distributed generation on distribution network protection and countermeasures, (2013)
  • [10] Li Tianjiao, The analysis of the impact of distributed generation on transmission network dispatching, (2014)
12345