Bi-LSTM based fault diagnosis scheme having high accuracy for Medium-Voltage Direct Current systems using pre- and post-processing

Diagnosing system faults is essential for ensuring the safety and reliability of medium-voltage direct current (MVDC) systems. In this regard, this study proposes a highly accurate artificial intelligence (AI)-based fault diagnosis scheme for MVDC systems. The proposed scheme pre-processes the measured voltage and current data using a discrete wavelet transform (DWT), considering a 60 × 100 2D window size. Subsequently, a bi-directional long short-term memory (Bi-LSTM) network is employed to diagnose and classify fault types and locations accurately. A stack method is applied in the data post-processing stage to achieve 100% fault diagnosis accuracy. The effectiveness of the proposed fault diagnosis scheme was verified by comparing its accuracy in a four-terminal MVDC system with that of existing schemes that employ other AI algorithms, such as CNN and LSTM. Moreover, the scalability of …