Identification method for power quality disturbances in distribution network based on transfer learning

• Published in: Archives of Electrical Engineering (Online) 2022-01, Vol.71 (3), p.731-754
• Main Authors: Heping, Peng, Wenxiong, Mo, Yong, Wang, Le, Luan, Zhong, Xu
• Format: Article
• Language: English
• Subjects: Accuracy; Classification; distribution network; disturbance identification; Freezing; Identification methods; Learning; multiple transfer learning; Noise levels; power quality; Signal quality; Training
• ISSN: 1427-4221; 2300-2506
• DOI: 10.24425/aee.2022.141682

For a higher classification accuracy of disturbance signals of power quality, a disturbance classification method for power quality based on gram angle field and multiple transfer learning is proposed in this paper. Firstly, the one-dimensional disturbance signal of power quality is transformed into a Gramian angular field (GAF) coded image by using the gram angle field, and then three ResNet 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 fused to train the classifier with a full connection layer, and a disturbance classification model for power quality is obtained. The simulation results show that the method has higher classification accuracy and better anti-noise performance, and the proposed model has good robustness and generalization.