Fault Detection and Classification in Medium Voltage DC Shipboard Power Systems With Wavelets and Artificial Neural Networks

TLDR

Future all‑electric ships’ MVDC power systems face new challenges, especially in fault detection and classification. The study proposes a fault detection and classification method for MVDC shipboard power systems using wavelet‑based multiresolution analysis combined with artificial neural networks. The method extracts fault‑related energy features via wavelet‑based multiresolution analysis and Parseval’s theorem, then classifies faults with an artificial neural network trained on simulated real‑time data processed in MATLAB. Using a Daubechies‑10 wavelet at scale 9, the approach achieved promising classification accuracy in simulations and was successfully implemented on a real‑time platform, demonstrating practical viability.

Abstract

This paper proposes a fault detection and classification method for medium voltage DC (MVDC) shipboard power systems (SPSs) by integrating wavelet transform (WT) multiresolution analysis (MRA) technique with artificial neural networks (ANNs). The MVDC system under consideration for future all-electric ships presents a range of new challenges, in particular the fault detection and classification issues addressed in this paper. The WT-MRA and Parseval's theorem are employed in this paper to extract the features of different faults. The energy variation of the fault signals at different resolution levels are chosen as the feature vectors. As a result of analysis and comparisons, the Daubechies 10 (db10) wavelet and scale 9 are the chosen wavelet function and decomposition level. Then, ANN is adopted to automatically classify the fault types according to the extracted features. Different fault types, such as short circuit faults on both dc bus and ac side, as well as ground fault, are analyzed and tested to verify the effectiveness of the proposed method. These faults are simulated in real time with a digital simulator and the data are then initially analyzed with MATLAB. The case study is a notional MVDC SPS model, and promising classification accuracy can be obtained according to simulation results. Finally, the proposed fault detection algorithm is implemented and tested on a real-time platform, which enables it for future practical use.