An Early Warning Method of TCU Failure in Electromagnetic Environment Based on Pattern Matching and Support Vector Regression

Xuan Li, Jianyong Zheng, Fei Mei, Haoyuan Sha and Danqi Li
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Xuan Li: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Jianyong Zheng: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Fei Mei: Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China
Haoyuan Sha: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Danqi Li: School of Electrical Engineering, Southeast University, Nanjing 210096, China

Energies, 2020, vol. 13, issue 21, 1-19

Abstract: With the continuous improvement of the voltage level of the power system, the electromagnetic interference problem of the converter station has become more and more serious. The thyristor control unit (TCU) is the core equipment of the converter valve, and its normal operation is related to the safe and stable operation of the entire converter valve. This paper starts with the actual electromagnetic environment in the converter valve hall, analyzes the failure principle of the TCU under electromagnetic disturbance, and observes the electromagnetic field distribution and sensitive components on the circuit board. Then, a TCU failure early warning method based on pattern matching and support vector regression (SVR) is proposed. The failure trend is deduced by constructing an abnormal information vector, and then the failure predictor is constructed using support vector regression optimized by grid search (GS), genetic algorithm (GA), and particle swarm optimization (PSO). Considering the failure type and warning time comprehensively, an early warning is issued when the failure mode probability increases to the threshold. When new failure modes appear, the failure mode library will continue to expand. The calculation example shows that this method can effectively warn the TCU failure in the electromagnetic environment, and its prediction accuracy can reach 89.2%, which is better than the traditional failure prediction method.

Keywords: TCU; SVR; electromagnetic disturbance; failure mode; CST (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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