Research on degradation analysis and health condition assessment method of phase shifter

Abstract

Abstract In high-power, high-reliability power supply systems, the switching operation of power devices is driven by phase shifters. Degradation or failure of the phase shifters leads to power device shoot-through and other failures, and reduces the power devices’ service lifetime. Aiming at the problem of phase shifter condition monitoring, a degradation model is proposed by analysing the degradation mechanism and aging process of the sensitive key components of the phase shifter, and then a health condition monitoring method with multi-dimensional features is presented in this paper. The multidimensional feature vectors in the time domain are extracted from the output voltage signals of different aging stages, and the time-domain feature separability of different health conditions is verified. Then the deep neural networks identification model based on deep learning technology is proposed to recognize the health condition of phase shifter. At last, the performance degradation simulation and experimental evaluation show that the proposed method can identify the phase shifter health conditions more accurately, and the accuracy rate can reach 95.892%.