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| Microgrid Fault Diagnosis Based on Deep Transfer Learning and LSTM Network |
| WU Zhong-qiang,LU Xue-qin |
| Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract A microgrid fault diagnosis method based on the combination of deep transfer learning and long-short-term memory network is proposed, which can be used to diagnose microgrids fault with different structures. First, wavelet packet transform is used to extract the feature vectors of fault as the input of the network; second, source domain data samples is used to pre-train the fault diagnosis model built by long short-term memory network, and relevant parameters is saved; Then transfer learning is used to migrate the parameters in the pre-training model to the domain adaptive network to obtain the combination model of deep transfer learning and long-short-term memory network; finally, the model is fine-tuned and migrated according to the labeled data (source domain data) and target domain data, and the single microgrid fault diagnosis model is transferred to other microgrids of different structures. The test results show that the proposed method can detect and identify any type of faults in microgrids of different structures. The mean square error of the identification result is 8.5905×10-5, smaller two orders of magnitude compared with the long-short-term memory network model before adaptive adjustment, the recognition effect is better and the diagnosis accuracy is obviously improved.
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Received: 26 April 2021
Published: 18 April 2023
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2023, Vol. 44 
