@ARTICLE{Heping_Peng_Identification_2022,
 author={Heping, Peng and Wenxiong, Mo and Yong, Wang and Le, Luan and Zhong, Xu},
 volume={vol. 71},
 number={No 3},
 journal={Archives of Electrical Engineering},
 pages={731-754},
 howpublished={online},
 year={2022},
 publisher={Polish Academy of Sciences},
 abstract={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.},
 type={Article},
 title={Identification method for power quality disturbances in distribution network based on transfer learning},
 URL={http://ochroma.man.poznan.pl/Content/124105/PDF/art13_i.pdf},
 doi={10.24425/aee.2022.141682},
 keywords={disturbance identification, distribution network, multiple transfer learning, power quality},
}