Research on the Fault Diagnosis Method of a Synchronous Condenser Based on the Multi-Scale Zooming Learning Framework

Baiyun Qian, Jinjun Huang, Xiaoxun Zhu (), Ruijun Wang, Xiang Lin, Ning Gao, Wei Li, Lijiang Dong and Wei Liu
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Baiyun Qian: State Grid Xinjiang Company Limited Electric Power Research Institute, Urumqi 830011, China
Jinjun Huang: State Grid Corporation of China, Beijing 100031, China
Xiaoxun Zhu: Department of Power Engineering, North China Electric Power University, Baoding 071003, China
Ruijun Wang: Department of Power Engineering, North China Electric Power University, Baoding 071003, China
Xiang Lin: State Grid Xinjiang Company Limited Electric Power Research Institute, Urumqi 830011, China
Ning Gao: Xinjiang Xinneng Group Co., Ltd., Urumqi Electric Power Construction and Commissioning Institute, Urumqi 830000, China
Wei Li: State Grid Xinjiang Company Limited Electric Power Research Institute, Urumqi 830011, China
Lijiang Dong: Xinjiang Xinneng Group Co., Ltd., Urumqi Electric Power Construction and Commissioning Institute, Urumqi 830000, China
Wei Liu: State Grid Xinjiang Company Limited Electric Power Research Institute, Urumqi 830011, China

Sustainability, 2022, vol. 14, issue 22, 1-14

Abstract: Under the background of the “strong direct current and weak alternating current” large power grid, the synchronous condenser with dynamic reactive power support capability becomes more important. Due to factors such as manufacturing, installation, and changes in operating conditions, there are many faults associated with the synchronous condenser. This paper studies a fault diagnosis method based on multi-scale zooming learning framework. First, through the energy fully connected (energy FC) layer, the synchronous condenser feature components of the fault signal of the camera are learned, and the transient features of the signal are enhanced. At the same time, the data is adaptively compressed and the effective features are mapped in a distributed manner. The faults are effectively diagnosed and isolated in advance. Secondly, a multi-scale learning framework is constructed to learn the multi-frequency features in the vibration signal. Finally, experiments show that the proposed method has certain advantages over the existing excellent models. The accuracy rate of diagnosis is higher than 99%.

Keywords: state detection; fault diagnosis; multi-scale; zooming learning; signal enhance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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