Transfer learning for fault section location of collector lines in offshore wind farms
Accurately locating the faulty section of collector lines is the key to the rapid recovery of collector sea cable faults in offshore wind farms,which is of great significance for safeguarding the economic interests of offshore wind farms.In the actual operation of offshore wind farms,a variety of unknown faults may occur,and the topology of the collector lines may change,and the data-driven collector line fault segment localization method based on the data will deteriorate in the application of actual wind farms.To address this problem,a collector line fault section localization method based on domain confrontation and graph convolutional neural network is proposed.In the new application scenario,the distribution difference between the old data and the new data can be reduced,and the extracted common features can make the model well adapted to the new scenario.Simulation results show that the proposed method has higher localization accuracy than traditional machine learning methods,and the domain adversarial migration learning mechanism significantly improves the model's adaptability in unknown scenarios.
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