期刊
SENSORS
卷 21, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/s21124159
关键词
MMC-HVDC; fault detection; fault classification; LSTM; BiLSTM; CNN; classification accuracy
资金
- National Natural Science Foundation of China [51105291]
- Shaanxi Provincial Science and Technology Agency [2020GY124, 2019GY-125, 2018JQ5127]
- Key Laboratory Project of the Department of Education of Shaanxi Province [19JS034, 18JS045]
A new method based on LSTM neural network is proposed for fault detection and classification in MMC-HVDC systems, achieving excellent performance in terms of accuracy and efficiency compared with other neural network methods such as BiLSTM, CNN, and AE-based DNN.
Fault detection and classification are two of the challenging tasks in Modular Multilevel Converters in High Voltage Direct Current (MMC-HVDC) systems. To directly classify the raw sensor data without certain feature extraction and classifier design, a long short-term memory (LSTM) neural network is proposed and used for seven states of the MMC-HVDC transmission power system simulated by Power Systems Computer Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC). It is observed that the LSTM method can detect faults with 100% accuracy and classify different faults as well as provide promising fault classification performance. Compared with a bidirectional LSTM (BiLSTM), the LSTM can get similar classification accuracy, requiring less training time and testing time. Compared with Convolutional Neural Networks (CNN) and AutoEncoder-based deep neural networks (AE-based DNN), the LSTM method can get better classification accuracy around the middle of the testing data proportion, but it needs more training time.
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