A Diagnostic Method for the Saturable Reactor Core Looseness Degree of Thyristor Converter Valves

Abstract

During the long-term operation of thyristor converter valves, the saturable reactor vibration (mainly caused by magnetostriction) will lead to core looseness faults. In order to accurately evaluate the fault degradation degree, this paper proposes a vibration signal recognition model for iron core looseness based on synchrosqueezed wavelet transforms and a convolutional neural network. Firstly, vibration experiments are conducted on saturable reactors to obtain signals under different core looseness degrees. Then, the spectrogram features of vibration signals are extracted using synchrosqueezed wavelet transform. Finally, based on the high-dimensional learning ability of convolutional neural networks, the fault characteristics of the spectrogram are mined to accurately identify the core looseness degree. The research results indicate that the model in the paper has higher recognition accuracy than some other methods, which provides convenience for the monitoring and maintenance of saturable reactors.