Fault diagnosis of ship diesel power distribution system based on WOA-RF algorithm

Objectives The marine diesel generator (DG) power distribution system is crucial for ship navigation. However, due to the harsh marine environment, frequent failures occur. Therefore, a fault diagnosis method based on Whale Optimization algorithm optimized random forest (WOA-RF) is proposed for the marine DG power distribution system.

Methods The marine DG power distribution system model is built using Matlab/Simulink simulation software. First, fault and normal condition data are collected. Then, the collected data is normalized, time-domain features are extracted, and important features are selected using Random Forest to reduce data dimensionality. Finally, the WOA-optimized Random Forest model is used for fault identification, diagnosis, and classification.

Results Simulation results show that the WOA-RF method can identify fault and normal states with 100% accuracy. It can distinguish 12 fault types with an accuracy of 98.26%. In the original dataset, the accuracy of WOA-RF improved by at least 4.86% and by up to 34.37% when compared to nine different algorithms. In the dataset with 10dB noise, the accuracy of WOA-RF improved by at least 2.43% and by up to 18.40% when compared to six different algorithms.

Conclusions The WOA-RF-based fault diagnosis method demonstrates superior accuracy and robustness in complex marine environments, providing a reliable solution for fault identification in marine power systems.