Power Quality Disturbance Classification Method Based on Improved Transfer Learning

Abstract

Aiming at the classification of power quality disturbance signals, a power quality disturbance classification method based on gram angle field and multiple transfer learning is proposed in this paper. Firstly, the one-dimensional power quality disturbance signal is transformed into GAF coded image by using gram angle field, and then three RESNET sub model networks are constructed. The disturbance signals with representative signal-to-noise ratios of 0 dB, 20 dB and 40 dB are selected as the input of the sub model to train the three sub models respectively. During this period, the training weights of the sub models are transferred in turn by using the method of multiple transfer learning. The pre training weight of the latter model is inherited from the training weight of the previous model, and the weight processing methods of partial freezing and partial fine-tuning are adopted to ensure the optimal training effect of the model. Finally, the features of the three sub models are used to train the full connection layer classifier, and finally a complete power quality disturbance classification model is obtained. Simulation results show that the method has good classification accuracy and anti noise performance, and the proposed model has good robustness and generalization.