To 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.

A 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).

After 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%.

The 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.

In order to ensure the safe and stable operation of a shipboard power system under fault conditions, a dynamic reconfiguration optimization method is proposed that considers time-varying load characteristics.

First, considering the topological structure, generation capacity limit, line current, node voltage and other constraints of the shipboard power system, a dynamic reconfiguration optimization strategy is proposed to minimize weight load cutting and voltage deviation. Next, an improved inertial particle swarm optimization algorithm is used to solve the optimization model. Finally, a typical shipboard power system is used as an example to verify the effectiveness of the proposed method.

The simulation results show that compared with the static reconfiguration method, the dynamic reconfiguration method can reduce the system's voltage deviation by 9.94%, thus significantly improving the quality of the network power supply.

The results of this study can provide references for the reliability design of shipboard power systems.

In order to improve the global localization accuracy and the real-time localization information in the cooperative localization of the autonomous underwater vehicle (AUV) system under clutter interference, a local information fusion algorithm based on information gain is proposed.

The gross error of the observed value is improved by the threshold weighting method, and then the local information is filtered to optimize the observed value so that it is closer to the true value. In this paper, the reliability of each piece of measurement information is investigated from the information entropy theory, and multiple sets of local filtering information of multiple master underwater vehicles are optimized. Taking the information gain as the weight, multiple sets of filtering results are fused to generate the unique localization information of the tested slave AUV. Furthermore, due to the communication delay in sonar detection and underwater acoustic signals between master and slave AUVs, filtering data asynchrony may occur in local information filtering and the information gain fusion algorithm. In view of this, a parallel structure of local information filtering and information gain weighting is proposed, which utilizes a real-time update mechanism to ensure that the input values of the information weighting algorithm are the latest output values of local filtering.

The simulation results show that compared with multi-source local filtering information, the proposed fusion method can effectively reduce the absolute error of local filtering, improve the localization accuracy, and optimize the local filtering.

The proposed fusion method can effectively realize the cooperative localization of the multi-AUV system.