@article{CHEN2026, 
author = {Siqing CHEN and Wengang JIANG and Zhiyu ZHU and Weipan WANG and Qian ZHANG},
title = {Research on predictive fault reconfiguration of ship power grid based on double-layer optimization strategy},
year = {2026},
journal = {Chinese Journal of Ship Research},
volume = {21},
number = {2},
pages = {391-403},
keywords = {ship power system, fault reconfiguration, objective optimization, dung beetle optimizer, hybrid improvement strategy},
url = {https://www.sciopen.com/article/10.19693/j.issn.1673-3185.04572},
doi = {10.19693/j.issn.1673-3185.04572},
abstract = {ObjectiveTo address the challenges of preventing non-random multiple concurrent faults caused by cable aging in shipboard power grids through preventive reconfiguration, and to resolve the issue of unreasonable weight coefficient settings in multi-objective reconfiguration models, thereby enhancing the safety and reconfiguration efficiency of shipboard power grids, a predictive fault reconfiguration method for shipboard power grids based on a double-level optimization strategy is proposed. MethodA cable aging fault prediction model for shipboard grids was constructed based on Markov chains and thermo-electro-mechanical multi physics analysis. This model was integrated as a constraint into the reconfiguration framework to avoid high-risk branches. A dual-layer optimization strategy was proposed: the upper layer dynamically solves multi-objective weight coefficients using the whale migration algorithm (WMA), while the lower layer determines the optimal switch configuration for grid reconfiguration using a multi-strategy-improved dung beetle optimizer (MSDBO). ResultsAfter integrating the fault prediction model, the reconfiguration strategy achieved 100% avoidance of high-risk branches (fault probability ≥0.5) proactively. Compared to the conventional two-step passive reconfiguration strategy, convergence speed improved by 47.06%. The dual-layer optimization framework enabled adaptive dynamic adjustment of weight coefficients and increased reconfiguration convergence speed by 56.25%. ConclusionThe integration of the cable aging fault prediction model and the dual-layer optimization framework effectively enables predictive reconfiguration of shipboard power grids. This approach proactively mitigates non-random faults while significantly improving reconfiguration efficiency and rationality. It offers a novel solution for addressing predictive reconfiguration challenges in non-random multiple-fault scenarios.}
}