Research on voiceprint recognition of reactor fault based on LSTM neural network

Abstract

High-voltage reactor is one of the critical equipment to ensure the safe and stable operation of power system. As the fault of high-voltage reactor is difficult to be identified accurately， a voiceprint recognition method based on LSTM （long short-term memory） neural network for high-voltage reactor faults is proposed. Firstly， the voiceprint signals generated during the operation of a high-voltage reactor are collected. Then the signals are divided into several segments， converted into a spectrogram， and the Mel time spectrum is used to reduce the dimensionality. Finally， the LSTM network is used to identify the high-voltage reactor faults in the spectrogram. The experimental results show that the proposed method can realize the intelligent diagnosis of high-voltage reactor faults， effectively improve the accuracy of fault identification， reduce the manpower required for fault detection， and improve the intelligent level of power grid safety monitoring.

Keywords

• high-voltage reactor
• lstm network
• voiceprint recognition
• fault
• spectrogram