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.

Natural disasters can split distribution networks into isolated microgrids, and the remaining local storage is often not enough to keep critical loads supplied until repairs start. Mobile emergency power vehicles (MEPVs) are useful in bridging this gap, but their dispatch is easily distorted when travel time is treated as static and battery charging is treated as constant-rate. This paper studies MEPV routing and load restoration under scenario-based road degradation and non-linear constant-current/constant-voltage (CC-CV) charging. A limited-communication setting is also stated explicitly, since post-disaster information is usually intermittent rather than fully real-time. The resulting scheduling problem is solved by an Improved Adaptive Harmony Search (IAHS) algorithm. IAHS keeps the harmony–memory structure but moves binary load decisions through a continuous latent space before threshold decoding, so that the differential update can use population differences without repeatedly disturbing load variables that have already become stable. Tests on a six-area system show higher average recovery and lower dispersion than HS, AHS, GHS, and AHS with standard BDE. Additional road-degradation sensitivity tests and single-MEPV scale-up tests show that the method remains feasible under stronger road deterioration and that computation time grows in a manageable way as the number of load variables increases.

Extreme disasters can simultaneously damage distribution equipment and communication links, preventing control centers from promptly acquiring complete energization information for critical nodes. This study formulates joint critical-node state identification in a fixed post-disaster radial topology as exact maximum a posteriori (MAP) inference in a tree-structured Bayesian network and proposes a path-cached branch-and-bound search (PCBS). PCBS combines admissible upper bounds for partial assignments, posterior-state discriminability ordering, bounded-width initial candidate construction, branch-and-bound pruning, and rollback-enabled path caching without changing the probabilistic model, candidate space, or MAP objective. Across 200 random scenarios on the IEEE 33-bus system, PCBS returned exactly the same MAP solutions and joint probabilities as exhaustive enumeration. On the IEEE 85-bus system with 14 critical nodes, the runtime speedup reached 28.3. When the evidence-quality score increased from 0.24 to 0.82, state accuracy rose from 78.30% to 92.40%, while runtime decreased from 0.714 s to 0.513 s. On the 300-node radial network, PCBS completed inference in 1.13 s on average with 30 critical nodes and 2.46 s with 60 critical nodes, while maintaining approximately 86.5–86.7% state accuracy. Hardware-in-the-loop experiments on a shipboard power system achieved 85.00% mean accuracy with a 1.31 s mean end-to-end inference time.

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.