Fault diagnosis of vibration signal of synchronous condenser based on wavelet model

Abstract

A large synchronous condenser has a complex structure and many parts, which is very prone to failure. However, the regular electrical quantity generally does not change significantly at the initial stage of failure, and may not reflect the fault characteristics. Some non-electrical quantities, such as vibration characteristics, can be used to diagnose and predict faults in a timely manner. Based on this, a multilayer-perceptron based wavelet model is proposed for fault diagnosis. Vibration signals are taken in real time at different positions on the surface of the condenser, and the energy eigenvalues are calculated by fast Fourier transform of the data, and the wavelet neural network model is input for training to obtain the nonlinear mapping relationship between the vibration signals and the fault types of the condenser, so as to realize the fault diagnosis of the condenser. Experimental results show that this method can effectively detect and diagnose the fault of the synchronous condenser in the early stage.