Fault Diagnosis in Shipboard Area Power Distribution Systems Based on PSO-SVM-CNN

Shipboard power systems are the core of maritime electrical automation, playing a crucial role in ensuring the normal operation of vessels. Efficient fault diagnosis facilitates rapid localization and judgment of faults, thereby safeguarding the safety of maritime operations. This research uses Simulink to simulate and analyze line faults in shipboard area distribution power systems. The simulation model is constructed to emulate common line faults in the power system. The study employs the Empirical Mode Decomposition (EMD) algorithm and Hilbert-Huang Transform as methods for extracting fault characteristics in shipboard power distribution systems. EMD is utilized to analyze the characteristic quantities of voltage and current fault signals, followed by the extraction of fault features from intrinsic mode functions through the Hilbert-Huang Transform. The extracted fault feature vectors serve as input parameters for a convolutional neural network (CNN) optimized using the particle swarm optimization (PSO) and support vector machine (SVM) to establish a fault diagnosis model. The analysis demonstrates that the PSO-SVM-CNN model significantly enhances the fault diagnosis accuracy of shipboard area power distribution systems, achieving a precision rate of up to 99.5%. This research contributes to advancing the safety and reliability of shipboard electrical systems through effective fault diagnosis methodologies.