Power Quality Disturbance Recognition based on Deep Neural Network and Adaptive Feature Fusion

Abstract

Aiming at the problems of low recognition accuracy and low noise resistance of current power quality disturbance recognition algorithms, a power quality disturbance recognition method based on the fusion of neural network and adaptive features is proposed. Firstly, one-dimensional features are extracted by 1D_CNN+GRU network. The GADF algorithm and 2D_CNN are used to extract 2D features, and then the extracted 1D features and 2D features are adaptively fused into a new feature. Finally, the new features are input into the channel attention mechanism and classified through the full connection layer. The experimental results show that the classification accuracy of the proposed method is above 92% for 40 disturbance types containing single, double, triple and quadruple disturbances, and above 96% for 20 disturbance types containing single and double disturbances.