The invention discloses an electric energy quality mixed disturbance analysis method based on deep learning. The power quality mixed disturbance classification method can effectively classify the power quality mixed disturbance, and has higher robustness. The method comprises the steps of firstly adjusting the attenuation rate of a Gaussian window function to change the time resolution and the frequency resolution of S transformation so as to embody the characteristics of different types of electric quality signals better and improve the distinguishing strength of the different types of electric quality signals; then constructing a deep learning model based on the LSTM, and classifying the the electric quality signals, in a constructed deep learning model, preprocessing the data by adopting a multi-layer perceptron, carrying out preliminary analysis and feature extraction on the data, and then, carrying out semantic segmentation on the electric quality signal by utilizing an LSTM artificial neural network with stronger analysis capability on sequence data, and finally, performing supervised classification on the semantic segmentation of the LSTM by adopting a pooling layer and a multi-layer perceptron, thereby facilitating the improvement of the classification capability of the model.
Electric energy quality mixed disturbance analysis method based on deep learning