When a fault occurs in a shipboard integrated power system, traditional reconfiguration strategies only adopt simple switching operations to change the system topology so as to isolate the faulty area. However, such strategies have limitations. For instance, under some specific operating conditions, the propulsion load is not allowed to lose power completely, and certain critical loads cannot tolerate power interruption. Considering that the propulsion load is a high-power load compared with other loads in the shipboard integrated power system, the navigation speed can be reduced according to the relationship between speed and power in the reconfiguration strategy to ensure the power supply of other loads. In addition, traditional optimization methods also suffer from drawbacks, such as being prone to local optima or failing to solve the fault reconfiguration problem of the shipboard integrated power system in real time. Therefore, this paper uses the DQN method to simulate and verify the proposed objective function under cruise operating conditions. The results demonstrate the effectiveness and real-time performance of the proposed method.

To address non-complete data issues such as missing, distorted, redundant and chaotic data in distribution network condition monitoring data easily caused by ship faults (e.g., sensor failure, cable short circuit, etc.), an improved optimization algorithm (IPS-SH5N1) considering the demand for incomplete information reconstruction in the distribution network of ship integrated power systems is proposed based on the SH5N1 meta-heuristic optimization algorithm. This algorithm is used to optimize the weights and thresholds of a BPNN, achieving accurate completion of non-complete information in the distribution network. The IPS-SH5N1-BPNN is applied to conduct information reconstruction verification on 2000 groups of incomplete data samples of ship integrated power systems. The results show that the information reconstruction accuracy (MSE) of this method is improved by more than 90% compared with the BPNN; the model training convergence time and global optimization time consumption are significantly shortened, and the average online single information reconstruction time is reduced to 4.45 ms. This method has core advantages of high precision, fast convergence, strong robustness and excellent real-time performance, which can provide reliable technical support for the intelligent operation and maintenance of ship integrated power systems.