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Article

Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method

1
School of Mechatronic Engineering, Xi’an Technological University, Xi’an 710021, China
2
Department of Electronic and Electrical Engineering, College of Engineering, Design and Physical Sciences, Brunel University, Uxbridge UB8 3PH, UK
3
State Grid Sichuan Electric Power Research Institute of China, Chengdu 610094, China
*
Author to whom correspondence should be addressed.
Academic Editors: Steven Chatterton and Lang Xu
Sensors 2021, 21(12), 4159; https://doi.org/10.3390/s21124159
Received: 8 May 2021 / Revised: 10 June 2021 / Accepted: 15 June 2021 / Published: 17 June 2021
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. View Full-Text
Keywords: MMC-HVDC; fault detection; fault classification; LSTM; BiLSTM; CNN; classification accuracy MMC-HVDC; fault detection; fault classification; LSTM; BiLSTM; CNN; classification accuracy
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MDPI and ACS Style

Wang, Q.; Yu, Y.; Ahmed, H.O.A.; Darwish, M.; Nandi, A.K. Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method. Sensors 2021, 21, 4159. https://doi.org/10.3390/s21124159

AMA Style

Wang Q, Yu Y, Ahmed HOA, Darwish M, Nandi AK. Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method. Sensors. 2021; 21(12):4159. https://doi.org/10.3390/s21124159

Chicago/Turabian Style

Wang, Qinghua, Yuexiao Yu, Hosameldin O.A. Ahmed, Mohamed Darwish, and Asoke K. Nandi 2021. "Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method" Sensors 21, no. 12: 4159. https://doi.org/10.3390/s21124159

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