Power Quality Disturbance Identification and Optimization Based on Machine Learning

In order to improve the electrical quality disturbance recognition ability of theneural network, this paper studies a depth learning-based power quality dis-turbance recognition and classification method: constructing a power qualityperturbation model, generating training set; construct depth neural network;profit training set to depth neural network training; verify the performance ofthe depth neural network; the results show that the training set is randomlyadded 20DB-50DB noise, even in the most serious 20dB noise conditions,it can reach more than 99% identification, this is a tradition. The methodis impossible to implement. Conclusion: the deepest learning-based powerquality disturbance identification and classification method overcomes thedisadvantage of the selection steps of artificial characteristics, poor robust-ness, which is beneficial to more accurately and quickly discover the categoryof power quality issues.