To address the acute contradiction between the extremely high power demands of high-energy pulsed weapons and the requirement for full-spectrum stealth capability, this paper systematically reviews research progress and challenges in integrating energy adaptation and stealth coordination within shipboard integrated power systems.

A systematic review methodology is employed to establish a three-dimensional collaborative analysis framework integrating energy, stealth, and intelligence. Within this framework, an in-depth assessment is provided of energy management strategies for pulsed power adaptation, global signal management techniques, and intelligent collaborative design methods based on digital twin technology. Furthermore, the study systematically reviews the current state of research on key technologies such as hybrid energy storage system topologies, multi-physics-based characteristic signal suppression, and cross-domain collaborative decision-making mechanisms.

In terms of energy matching, hybrid energy storage systems are widely recognized as a mainstream solution for mitigating pulsed power impacts and ensuring grid stability. Regarding stealth coordination, active control technologies such as digital degaussing and active noise control are increasingly replacing traditional passive suppression methods. At the system integration level, the introduction of artificial intelligence and digital twin technologies shows strong potential for addressing challenges related to millisecond-level dynamic response requirements and inefficient R&D iteration cycles.

Theoretical research and technical analysis indicate that establishing an integrated collaborative design framework for precise energy supply and intelligent signal control is a fundamental approach to addressing compatibility challenges associated with high-energy weapons aboard naval vessels. In particular, intelligent dynamic trade-off control and cross-domain collaborative optimization are expected to become key future research directions, carrying significant strategic importance for enhancing the combat effectiveness and survivability of next-generation naval vessels.

To address the problems of high network losses during the operation of islanded distribution networks with a high penetration of distributed power sources and the slow convergence and poor stability of existing algorithms used for network reconfiguration, a dynamic reconfiguration strategy for island distribution networks based on an improved grey wolf optimization (GWO) algorithm is proposed, with the primary objective of minimizing network losses.

A dynamic reconfiguration model for islanded distribution networks is established, with the minimization of active network losses and voltage deviation as the optimization objectives. To enhance the global search capability and convergence efficiency of the GWO algorithm, several strategies are introduced, including probabilistic perturbation, a dynamic tabu list, and adaptive parameter adjustment.

The results of the dynamic reconfiguration modelling and simulation of an islanded distribution network with a high penetration of distributed power sources show that the improved GWO algorithm achieves better stability, higher accuracy and greater computational efficiency compared to other algorithms, including the original GWO algorithm and the improved particle swarm optimization (PSO) algorithm. Under static reconfiguration conditions, the active power loss in the islanded distribution network is reduced by 21.8%, and the minimum bus voltage is increased by 2.03%. Under dynamic reconfiguration strategy, the active power loss is reduced by 27.98% over a 24-hour period. Furthermore, under extreme weather and line fault scenarios, the reconfiguration strategy continues to ensure stable network operation, with intra-day power losses reduced by 22.16% and 26.30%, respectively.

The results show that the improved GWO algorithm provides a novel theoretical framework and optimization approach for the dynamic reconfiguration of islanded distribution networks.

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.

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.

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

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.

This paper proposes using the Spider Wasp Optimization (SWO) algorithm to achieve precise position control of an electro-hydrostatic actuator (EHA) while reducing the parameter tuning difficulty of the EHA sliding mode controller and improving its comprehensive performance.

A simplified model of an EHA is established and its sliding mode controller is designed. The sliding mode surface and reaching rate parameters of the controller are then adjusted by the SWO algorithm, and Matlab/Simulink and AMEsim simulation software is used to build a co-simulation model for verification.

The parameters of the sliding mode controller are adjusted manually and by the SWO algorithm respectively. The comparative simulation results show that the sliding mode controller optimized by the SWO algorithm avoids overshoot and has enhanced anti-interference properties, and its convergence speed is improved by 33.6%, proving the feasibility of optimizing the sliding surface and reaching rate parameters of an EHA sliding mode controller using the SWO algorithm.

The results of this study can provide theoretical references for the design of EHA sliding mode controllers.

Aiming at the stability problem of naval DC propulsion systems with a significant proportion of power electronic devices, a stability analysis and controller parameter optimization method based on the Middlebrook impedance ratio stability criterion is proposed.

First, the small-signal output impedance of the DC bus and small-signal input impedance of the inverter and motor are deduced and established through the simplification of the system model, linearization of the nonlinear system, dq transformation of the three-phase AC system, etc. The controller parameters are then optimized using a particle swarm algorithm to minimize the value of support capacitance on the condition of ensuring system stability. Finally, a simulation model of the DC propulsion system is constructed to simulate and analyze the influence of the support capacitance and equivalent resistance and inductance of the DC bus on the stability of the system.

The simulation results show that the system's stability margin is obviously improved after the controller parameters are optimized by the particle swarm algorithm, which in turn lowers the value of support capacitance and reduces the overall size of the DC propulsion system's converter.

The findings of this study can provide useful references for research on voltage oscillations and their suppression in integr-ated power systems with a significant proportion of power electronic devices.

In order to improve the rapidity and accuracy of fault location for island and reef power supply systems composed of multiple distributed power sources, a fault location method based on an immune model and improved immune algorithm is proposed.

First, the immune model is used to divide the island and reef power supply system into several cell sections containing monitoring points. According to the monitored fault current signal, the cell section where the fault point is located is determined in order to minimize the fault determination range. The fault points of the cell section are then coded and the antibody population is generated by the improved immune algorithm, enabling the fault section to be quickly and accurately located for a second time, thus greatly reducing the calculation load.

According to the Matlab simulation results of a typical island and reef power supply system, the immune model and improved immune algorithm combination can realize rapid fault location under various fault conditions, with such advantages as high positioning accuracy, light calculation load and fast solution speed.

The results of this study can provide useful references for the design of island and reef power supply systems.

Aiming to address the existing challenges in the power capacity configuration of island and reef hybrid power generation systems, this paper proposes an optimization method based on the adaptive ant colony algorithm (ACA).

An ACA is used as the core optimization tool to configure the power capacity of an island and reef hybrid power generation system. The process of ants foraging is simulated by employing the ACA and using the power generation of renewable energy as dynamic pheromones in the search space. The optimal solution is then found through global search, achieving the full utilization of renewable energy. Taking Wai Lingding Island as the target island, a 'wind-solar-diesel-storage' microgrid hybrid power generation system model is constructed, and the ACA is used to optimize its capacity configuration.

The simulation results of the algorithm indicate that, compared to the improved Grey Wolf algorithm and Artificial Bee Colony algorithm, the ACA can effectively reduce the operational costs and environmental pollution of the microgrid hybrid power generation system, while ensuring the stability of the power supply.

The results of this study can effectively increase the power supply stability of the microgrid hybrid power generation system, reduce operating costs and environmental pollution, and thus achieve efficient utilization of energy resources.

For a marine 690 Vac bidirectional converter based on a three-level flying capacitor, in order to make it start stably and transition to its normal working state, the flying capacitor must be pre-charged. This paper proposes a pre-charging strategy based on single-tube control which is suitable for three-level flying capacitors.

The on-off of the three-level outer tube is controlled on the basis of the flying capacitor, and efficient and fast pre-charging control is performed.

When this strategy is applied to a 690 Vac bidirectional converter, the pre-charging of the flying capacitor is completed within 1 second in Buck mode and less than 5 seconds in Boost mode. After pre-charging, the voltage of the flying capacitor remains at the value of half of the voltage of the high voltage side, and the voltage of the input side capacitor remains at the value of the input side voltage.

After experimental verification, the proposed pre-charging strategy based on controlling the outer tube of the three-level flying capacitor can be applied to a 690 Vac bidirectional converter device without the need for additional pre-charging power devices, which not only simplifies the topology but also ensures the reliable and fast pre-charging of flying capacitors.

In order to make the energy in the degaussing coil feed back to the degaussing power supply and improve the energy utilization rate, a degaussing power supply control strategy is proposed with a hybrid supercapacitor and lithium battery power supply.

After studying the electrical characteristics of the degaussing coil under strong coupling conditions, the hybrid power supply system is introduced. The change of the degaussing coil current is divided into three stages: rising, holding and falling (energy feedback). In different stages of degaussing coil current change, the opening and closing of the corresponding switches are controlled. The simulation is carried out using Matlab/Simulink, and the simulation results are analyzed to verify the feasibility of the energy feedback control strategy based on the hybrid power supply system.

The analysis and simulation results show that the total current flowing through each degaussing coil can quickly rise to and stabilize near the standard value, the overshoot is small and the energy supplied to the degaussing coil can also be fed back into the supercapacitor.

The proposed degaussing system with a hybrid power supply can feed back the energy in the degaussing coil to the supercapacitor, thereby reducing the energy loss of the whole degaussing system.

In order to realize the precise control of a certain type of electro-hydraulic position servo system, a feedback-feedforward variable gain iterative learning method is proposed.

First, the simplified model of the electro-hydraulic position servo system is established, then the iterative learning control algorithm is improved and a variable gain learning law with a forgetting factor is adopted. Finally, Matlab simulation is carried out for verification.

The simulation results show that compared with traditional iterative learning and PID control, the improved iterative learning algorithm has better convergence and smaller tracking error, and can make the electro-hydraulic position servo system track the position curve quickly and accurately with improved dynamic characteristics.

The results of this paper can provide references for the control performance optimization and practical engineering application of electro-hydraulic position servo systems.