Search Results (98)

Reliable electric-power restoration is important for autonomous ships because propulsion, navigation, communication, and emergency loads depend on a compact shipboard distribution network with limited generation redundancy. This paper studies the fault reconfiguration of an autonomous shipboard meshed microgrid under generator outage, branch fault, and dynamic-load disturbance conditions. A multi-objective model is established by considering priority-based load restoration, switching-operation cost, and generator load balancing. To represent emergency load management more realistically, a continuous restoration ratio is introduced for aggregated shipboard load groups, so that full restoration, derated operation, and load shedding can be described in one formulation. A Q-learning-guided ant colony optimization method (QL-ACO) is then proposed. In this method, Q-learning is used as an adaptive parameter controller for the pheromone factor, heuristic factor, and greedy selection probability, rather than as a direct switch-action selector. Elite reinforcement and pheromone smoothing are also introduced to reduce premature convergence. Four shipboard fault scenarios are simulated, including a single-branch fault, a single-generator outage, a combined branch–generator fault, and a dynamic-load–branch-fault case. The results show that the proposed method maintains critical-load restoration, improves Class-III load recovery in complex scenarios and obtains feasible reconfiguration schemes with fewer switching operations than fixed-parameter ACO, NSGA-II, PSO, and a compact direct RL reference baseline. Runtime, scalability, statistical, and sensitivity analyses are also provided to examine online applicability and robustness. Full article

Energy and power management systems (EMS/PMS) are essential for electric-propulsion ships, affecting propulsion performance, fuel consumption, emissions, and component lifetime. As shipboard power systems integrate heterogeneous energy resources and face nonlinearity, uncertain load demand, and multi-source interactions, deep reinforcement learning (DRL) has emerged as a promising adaptive, sequential decision-making tool in shipboard EMS/PMS. This perspective reviews DRL studies through a hierarchical decision-making framework comprising power dispatch, energy coordination, and operational strategy. Most research focuses on real-time power dispatch, while emerging research addresses energy coordination via multi-source cooperation, multi-objective operation, degradation awareness, and uncertainty handling. However, operational strategy remains underexplored, despite its role in speed control, route-aware planning, predictive operation, and voyage scheduling. This paper argues that future shipboard EMS/PMS adopt integrated hierarchical DRL frameworks across all three decision layers, leveraging DRL’s strengths in sequential policy learning in dynamic environments and supporting multi-time-scale decision-making. This paper clarifies current research trends, identifies gaps, and outlines future directions toward adaptive, reliable, and autonomous shipboard EMS/PMS in next-generation electric-propulsion ships. Full article

Reliable battery state awareness is essential for energy management and power allocation in hybrid electric ships. However, battery management systems are increasingly exposed to False Data Injection Attacks (FDIAs) in intelligent connected environments, which can distort State of Charge (SOC) estimation and compromise the operational reliability of shipboard power systems. To address this challenge, this paper proposes a closed-loop “Modeling-Detection-Defense” framework for secure SOC estimation in marine cyber-physical energy systems. First, a stealthy FDIA model is developed based on battery dynamics and physical consistency constraints. Second, a hybrid detection method combining unsupervised and supervised learning is proposed to identify attacks. Finally, a long short-term memory network is employed to reconstruct compromised measurements and provide reliable SOC information for continuous energy management. Experimental results demonstrate that the proposed framework mitigates SOC estimation deviations caused by FDIAs. In addition, it effectively reduces power allocation errors and energy losses, thereby improving the cyber-resilience, operational reliability, and energy efficiency of hybrid ship power systems. Full article

Shipboard DC microgrids face critical stability challenges including low-inertia-induced voltage fluctuations and impedance mismatch caused by constant power loads (CPLs), which severely threaten system stability. Existing virtual DC machine (VDCM) control methods typically treat inertia support and impedance optimization as separate design objectives, lacking a unified frequency-domain design framework. To address this issue, this paper first establishes an accurate virtual impedance model for the standard VDCM controller, quantitatively revealing how its control parameters (J and D) shape the frequency-domain impedance characteristics and identifying potential stability conflicts. Building upon this model, a feedforward-compensated VDCM (FFC-VDCM) strategy is proposed, introducing a differential feedforward loop to actively reshape the converter output impedance in the critical mid-frequency range without interfering with the inertia support function. The impedance reshaping effect is quantified via impedance-based stability analysis; the proposed method improves the gain margin from $4.1$ dB (with conventional VDCM) to $8.6$ dB, along with a significant enhancement in the phase margin, confirming improved system robustness. Hardware-in-the-loop (HIL) experiments conducted under realistic shipboard conditions further confirm the theoretical analysis, demonstrating superior transient voltage regulation and validating the practical effectiveness of the proposed strategy. The FFC-VDCM provides a synergistic solution for concurrently improving inertia and stability in low-inertia DC microgrids. Full article

This paper presents a complete analysis and simulation of the operation of a zero-emission marine vessel with electric propulsion. A hypothetical passenger ferry operating in the Aegean Sea, Greece, is considered, which is powered by a hydrogen fuel cell, a battery energy storage system (BESS) and photovoltaic (PV) energy. Wind-assisted ship propulsion (WASP) is employed to reduce the energy consumption of the ship. A complete analysis is performed, which includes optimal energy management, dynamic analysis and emerging stability concerns due to the high integration of power electronic converters in the shipboard microgrid. The energy management system (EMS) applies multi-objective optimization based on the corona virus optimization (CVO) algorithm and the teaching–learning-based optimization algorithm (TLBO). The dynamic behavior of the microgrid is tested using real-time digital simulations. Converter-driven stability issues are investigated, which may arise due to interactions among the various converter controllers and passive components of the microgrid. Full article

Shipboard diesel–electric power systems (SDEPSs) are inherently vulnerable to transient instability owing to their compact, isolated, and low-inertia design. Their performance is considerably influenced by dynamic disturbances, which can lead to operational failures and accidents of varying severity. Therefore, this research addresses the critical challenge of transient stability enhancement in SDEPSs during significant dynamic disturbances. Recognizing that traditional automation and protection systems respond only after transient instability occurs, this study introduces an emergency preventive control (EPC) strategy that enables anticipatory control of SDEPS power sources to enhance transient stability. The proposed EPC system integrates hardware and software components to perform real-time monitoring and control based on forecasting system parameters, specifically the relative rotor angles of the power sources. The feasibility and effectiveness of the proposed system are validated through comprehensive computer simulations, demonstrating improvements in transient stability and system resilience by substantially reducing relative rotor angle deviations during the transient event. Overall, the proposed framework can be readily integrated into existing shipboard control architectures, offering an effective means to improve the safety of modern SDEPSs operating under dynamic conditions. Full article

A real-time optimized model predictive control–equivalent consumption minimization strategy (MPC-ECMS) is proposed for the energy management of shipboard gas turbine–photovoltaic hybrid energy storage (GT-PV-HESS) power systems. Different from conventional MPC-ECMS methods that only adopt single-level SOC-based feedback regulation, the strategy aims to overcome the limitations of conventional methods, including the poor adaptability of rule-based strategies and the lack of foresight in traditional ECMS, which cannot achieve simultaneous improvements in fuel economy, generation efficiency, and battery lifespan while maintaining system stability under dynamic operating conditions. The proposed strategy integrates the forward-looking optimization ability of MPC and the real-time decision-making advantage of ECMS. MPC is used to predict short-term load and photovoltaic power and identify operating modes, and a two-level equivalent factor adjustment mechanism is designed based on predicted conditions and battery state of charge (SOC). The optimized factor is applied in ECMS to achieve optimal power allocation between the gas turbine and battery under system constraints, while the supercapacitor implements power secondary correction to suppress bus voltage fluctuations caused by gas turbine operation. The architectural novelty lies in the two-level coordination mechanism and the marine-oriented hybrid energy storage cooperation. Simulation studies are conducted on the MATLAB/Simulink R2021b platform, and the results validate that it yields superior performance to the rule-based control and traditional ECMS under typical ship operating conditions. It increases gas turbine efficiency to 15.62% (0.47% and 6.24% higher than the two conventional methods). Over the 120 s simulation period, the proposed strategy reduces total fuel consumption to 1.049 kg, which is lower than 1.054 kg for the rule-based strategy and 1.192 kg for conventional ECMS. The battery SOC fluctuation is restricted to only 3.89%. The maximum DC bus voltage fluctuation rate is controlled within 3.28%, which meets the stability requirements of shipboard DC microgrids. The proposed strategy achieves a comprehensive and superior balance among fuel economy, power generation efficiency, and battery life while ensuring stable system operation under all working conditions. This two-level MPC-ECMS framework provides a high-performance and practically feasible energy management solution for shipboard hybrid power systems. Full article

This study presents an optimal strategy for hybrid electric propulsion ships with variable-speed diesel generators (VSDGs) and proton exchange membrane fuel cells (PEMFCs). With the International Maritime Organization imposing stricter environmental regulations, shipboard power systems must satisfy emission limits and operational constraints cost-effectively. To address this challenge, a Lagrangian relaxation (LR)-based optimization framework integrating unit commitment and economic dispatch is developed. Practical operational constraints reflecting realistic shipboard conditions are incorporated. The effectiveness of the proposed framework was evaluated through simulation-based case studies under various realistic operating conditions. Simulation results show that the proposed LR framework achieves lower total fuel costs than conventional priority-list methods while complying with environmental regulations under diverse operating scenarios. Full article

Shipboard power systems are essential to the safe and stable operation of marine vessels, while short-circuit faults may lead to equipment damage and system interruption under complex onboard operating conditions. To improve fault diagnosis performance in this setting, this study proposes an interpretable short-circuit fault diagnosis framework that combines a multi-scale CNN-LSTM model with Shapley value analysis. Relative changes between pre-fault and fault-state electrical signals are used to construct the input representation, which helps characterize fault-related variations more effectively. The multi-scale convolution branches extract patterns associated with different temporal ranges, and the LSTM layer further models their sequential dependence. Shapley value analysis is introduced to quantify the contribution of voltage- and current-related features, identify the most informative inputs, and support feature screening. Experiments on a Simulink-based shipboard power system dataset show that the proposed method achieves competitive fault diagnosis performance compared with baseline models, including CNN, LSTM, and LightGRU. Under repeated runs, the proposed framework attains an average diagnostic accuracy of 99.03 ± 0.20%, while also maintaining strong precision, recall, and F1-score performance. Under the tested noise conditions, it shows better robustness than the comparison methods. These results indicate that the proposed framework can provide accurate and interpretable fault diagnosis for shipboard power systems. Full article

The rapid development of cyber–physical technologies has led to enhanced observability and controllability of shipboard power systems. However, the reliance of shipboard power systems on information networks undermines the traditional security provided by physical isolation; under malicious attacks, faults in the information domain can propagate rapidly, causing physical power outages and reducing the resilience of shipboard power systems. To address this issue, this paper investigates the cascading failure reconstruction and resilience enhancement in shipboard cyber–physical systems (SCPSs) under uncertain network attacks. First, a cascading failure propagation model is established to capture the interaction between attack paths and system vulnerabilities, revealing how cyberattacks spread through communication links and infiltrate the power topology. Then, a reinforcement learning-based load recovery strategy is developed, in which a masked proximal policy optimization (masked-PPO) algorithm is employed to optimize reconfiguration decisions under operational constraints. The proposed approach enables adaptive and efficient recovery actions in complex cross-domain environments. Case studies based on representative SCPS scenarios demonstrate that the proposed method improves cascading-failure reconfiguration capability by 13.21% and reduces the average decision time by 18.6%, validating its effectiveness, real-time performance, and scalability. Full article

Ship propulsion electrification is an important enabler towards a sustainable shipping industry. Ship power systems are turning into modern microgrids integrating different generation/storage resources, converter technologies and electric propulsion, utilizing different control levels and communication systems. The definition of comprehensive test requirements, set-ups and procedures is critical to ensure that the equipment will behave as expected in the ship system context. Comprehensive testing is becoming increasingly challenging due to complex interactions at the system level, attributed to electrical, mechanical/hydrodynamic, control, protection, and information and communication systems present in modern and future ships. Standardization has addressed the testing of several individual components, as well as specific system tests for marine applications; however, a holistic testing approach is missing. This paper reviews the generic and maritime standards for testing ship electric power propulsion systems and equipment, focusing on generators/motors, power electronic drives and onshore power supply systems. A review of the scientific literature is performed, classifying the publications according to the testing method, such as pure hardware tests, co-simulation and hardware in the loop simulation (HIL). The need for holistic testing of shipboard microgrids is explained. A holistic HIL testing approach is proposed, which integrates hardware controllers and power equipment of different manufacturers and functions, in order to reduce the complexity and cost of sea trials. The proposed approach is accompanied by example implementation and application guidelines. Full article

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. Full article

To meet the dual requirements of selectivity and rapidity in fuse-based short-circuit protection for shipboard DC Integrated Power Systems (DC IPS), this paper proposes a novel coordination method. This approach employs an artificial neural network (ANN) to map the inherent time–current characteristic (TCC) curves of all fuses onto a unified time–current coordinate plane. Protection selectivity is then evaluated based on the relative positions of these curves, and by prioritizing fuses with shorter operating times, both selectivity and rapid fault clearance are achieved. Furthermore, through a mathematical analysis of the current relationships between faulted and non-faulted distribution circuits, the ANN is formulated to require only current and time data while maintaining robustness to moderate variations in short-circuit transition resistance. The effectiveness of the proposed method is validated using DC IPS cases of a hybrid passenger vessel and a pure electric sightseeing vessel. Compared with conventional coordination methods, the proposed method simultaneously accounts for the TCCs of protective devices and the influence of transition resistance on short-circuit current behavior. The case study results demonstrate that the proposed method achieves both selective and rapid protection, and shows strong potential for broader application in the coordination of multi-source DC power systems. Full article

This paper presents a systematic methodology for time-domain modeling of three-phase power transformers aimed at electromagnetic transient analysis in shipboard and embedded electrical systems. Accurate modeling of transformers in such environments is critical, as naval power systems are subject to strict electromagnetic compatibility (EMC) requirements and are particularly susceptible to fast transients caused by switching operations, fault events, and nonlinear loads operating in confined and isolated grids. The proposed approach combines the Vector Fitting (VF) algorithm with Clarke modal decomposition to obtain stable, passive, and causal rational approximations of the frequency-dependent admittance matrix over a wide frequency range. The admittance matrix is first identified from frequency-domain measurements or simulations, capturing the transformer’s terminal behavior across multiple frequency sub-bands. Clarke’s transformation is then applied to decouple the three-phase system into independent modal components—namely the zero-sequence and positive-sequence modes, reducing the original multi-phase problem to a set of independent single-phase systems. This modal decoupling significantly improves computational efficiency without sacrificing accuracy, as each mode can be fitted and simulated independently. Full article

Solid oxide fuel cell (SOFC) systems in shipboard power plants exhibit strong thermal–electrochemical coupling and are highly sensitive to both balance-of-plant and stack-related faults under changing operating conditions. In this study, a mechanism–data fusion dynamic model of a standalone SOFC system is developed in MATLAB/Simulink by integrating electrochemical equations with mass, species, and energy conservation and key balance-of-plant components. The model is validated against experimental data, with errors of 0.4–2.8%. Based on the validated model, fuel leakage and electrode delamination are introduced to investigate compound and sequential cross-condition faults. The present results show that fuel leakage causes the most severe degradation in current, power, and temperature, whereas electrode delamination mainly reduces current and power by decreasing the effective reaction area. Compound and sequential faults exhibit non-superimposable dynamic evolution, indicating significant fault interaction effects. A partially monotone decision tree combined with point-biserial correlation is then applied for fault diagnosis. The overall diagnostic accuracy for compound faults reaches 88.5%, while the proposed segmented cross-condition strategy improves the peak accuracy for sequential faults to 87.5%. These results provide an effective framework for SOFC fault modeling and diagnosis under variable operating conditions. Full article