Diagnostic Method for Short Circuit Faults at the Generator End of Ship Power Systems Based on MWDN and Deep-Gated RNN-FCN

Abstract

Synchronous generators with three phases are crucial components of modern integrated power systems in ships. These generators provide power for the entire operation of the vessel. Therefore, it is of paramount importance to diagnose short-circuit faults at the generator terminal in the ship’s power system to ensure the safe and stable operation of modern ships. In this study, a generator terminal short-circuit fault diagnosis method is proposed based on a hybrid model that combines the Multi-Level Wavelet Decomposition Network, Deep-Gated Recurrent Neural Network, and Fully Convolutional Network. Firstly, the Multi-Level Wavelet Decomposition Network is used to decompose and denoise the collected electrical signals, thus dividing them into sub-signals and extracting their time-domain and frequency-domain features. Secondly, synthetic oversampling based on Gaussian random variables is employed to address the problem of imbalance between normal data and fault data, resulting in a balanced dataset. Finally, the dataset is fed into the hybrid model of the Deep-Gated Recurrent Neural Network and Fully Convolutional Network for feature extraction and classification of faults, ultimately outputting the fault diagnosis results. To validate the performance of the proposed method, simulations and comparative analysis with other algorithms are conducted on the fault diagnosis method. The proposed algorithm’s accuracy reaches 96.82%, precision reaches 97.35%, and the area under curve reaches 0.85, indicating accurate feature extraction and classification for identifying short-circuit faults at the generator terminals.

Advanced State Estimation Approach for Partially Observable Shipboard Power Systems

“…A cloud computing-supported algorithm was presented for the complex querying of extensive ship failure data, focusing on query performance prediction [11]. A hybrid fault diagnosis method for SPS generator-end short-circuit faults was proposed, integrating multi-level wavelet decomposition networks, deep gated recurrent neural networks, and fully convolutional networks [12]. Vector quantization feature coding technology was employed for distributed storage structure analysis of extensive ship failure data, and segmented adaptive regression analysis was employed for spectral feature analysis [13].…”

A Novel Multi–Scale One–Dimensional Convolutional Neural Network for Intelligent Fault Diagnosis of Centrifugal Pumps

“…Fault diagnosis based on mechanical vibration data can be categorized into two types [6]. One involves establishing mathematical models for simulation verification, and many successful model-based methods have been developed and applied [7]. These methods, compared to visual inspection, significantly save time and effort in fault diagnosis.…”