The Development of Bi-LSTM Based on Fault Diagnosis Scheme in MVDC System

Jae-Sung Lim, Haesong Cho, Dohoon Kwon () and Junho Hong
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Jae-Sung Lim: Korea Electrotechnology Research Institute (KERI), Ansan-si 15588, Gyeonggi-do, Republic of Korea
Haesong Cho: Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Dohoon Kwon: Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Junho Hong: Department of Electrical and Computer Engineering, University of Michigan Dearborn, Dearborn, MI 48128, USA

Energies, 2024, vol. 17, issue 18, 1-15

Abstract: Diagnosing faults is crucial for ensuring the safety and reliability of medium-voltage direct current (MVDC) systems. In this study, we propose a bidirectional long short-term memory (Bi-LSTM)-based fault diagnosis scheme for the accurate classification of faults occurring in MVDC systems. First, to ensure stability in case a fault occurs, we modeled an MVDC system that included a resistor-based fault current limiter (R-FCL) and a direct current circuit breaker (DCCB). A discrete wavelet transform (DWT) extracted the transient voltages and currents measured using DC lines and AC grids in the frequency–time domain. Based on the digital signal normalized by the DWT, using the measurement data, the Bi-LSTM algorithm was used to classify and learn the types and locations of faults, such as DC line (PTP, P-PTG, and N-PTG) and internal inverter faults. The effectiveness of the proposed fault diagnosis scheme was validated through comparative analysis within the four-terminal MVDC system, demonstrating superior accuracy and a faster diagnosis time compared to those of the existing schemes that utilize other AI algorithms, such as the CNN and LSTM. According to the test results, the proposed fault diagnosis scheme detects MVDC faults and shows a high recognition accuracy of 97.7%. Additionally, when applying the Bi-LSTM-based fault diagnosis scheme, it was confirmed that not only the training diagnosis time (TraDT) but also the average diagnosis time (AvgDT) were 0.03 ms and 0.05 ms faster than LSTM and CNN, respectively. The results validate the superior fault clarification and fast diagnosis performance of the proposed scheme over those of the other methods.

Keywords: Bi-LSTM; DCCB; DC fault; discrete wavelet transform; inverter fault; MVDC system; R-FCL (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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