Simulation of Substation Switch Fault Monitoring Based on Mel Filter
The signal characteristics of substation switch faults have a wide range,and there are problems of con-fusion and unclear characteristics.This paper presents a method of monitoring substation switch fault conditions based on sound signals.The probability density function is used to calculate the abnormal operation probability of substation switchgear and determine the key monitoring switchgear.Pre-emphasis and frame processing are carried out for sub-station switchgear signals,and a Mel filter is established to extract MFCC features of substation switchgear signals.MFCC features are input into the learning vector neural network(LVQ)to output the fault state monitoring results,and a backtracking search algorithm is introduced to optimize the initial weight of the learning vector neural network to a-chieve the substation switch fault state monitoring.The simulation results show that the proposed method has good fault condition monitoring effect.
Substation switchProbability density functionLearning vector neural networkMeier filterFault condition monitoring
万方数据
维普
