Machine learning for power system protection and control |
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| Authors: | Hanyu Yang Xubin Liu Di Zhang Tao Chen Canbing Li Wentao Huang |
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| Affiliation: | 1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China;2. School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430073, China;3. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China |
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| Abstract: | Since the power system is undergoing a transition into a more flexible and complex system, it urges improvements in fault diagnosis techniques for the power system protection to avoid cascading damages at the occurrence of faults. Facing with challenges of massive data, several machine-learning based methods for identifying faults were proposed over the past years. In this paper, an overview of conventional and trending machine learning applications for the fault diagnosis are summarized. |
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| Keywords: | Fault diagnosis Machine learning Deep learning Power system |
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