Search Results (37)

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

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

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

In this paper, the dynamic modeling and integrated simulation of a ship microgrid system designed to enhance power quality and energy efficiency in electric propulsion vessels are proposed. The proposed system consists of a photovoltaic (PV) array, a battery energy storage system (BESS), a diesel generator, and a propulsion system, all of which are organically integrated through power conversion devices. To compensate for the intermittent nature of solar power, a control strategy featuring Maximum Power Point Tracking (MPPT) for the PV system and bidirectional DC/DC converter control for the battery was implemented. Specifically, a control logic to stabilize the system output in response to the fluctuating loads of the electric propulsion system was developed using PSCAD (v50) software. The simulation results demonstrate that the proposed control strategy maintains DC-link voltage deviation within ±1.8% and achieves a settling time of less than 0.8 s while optimizing propulsion efficiency (peak-shaving ratio 25–30%) under both constant and variable speed operating conditions. Battery SOC variation is limited to 18–88%, preventing overcharge or discharge. This research provides a foundational framework for the design of energy management systems (EMSs) and grid stability assessments for future eco-friendly electric propulsion ships. Full article

The accurate prediction of gas turbine output power is critical for flexible scheduling and shipboard microgrid resilience. However, purely data-driven models suffer from poor generalization and physical inconsistency in complex marine environments, especially under unseen operation conditions. This paper proposes a condition-adaptive physics-informed long short-term memory (CAPI-LSTM) framework to ensure physical consistency across the full operation envelope. In the proposed framework, an MLP-based condition-adaptive regulator is developed to dynamically adjust the compressor air flow rate within the embedded physics-informed loss function. The proposed CAPI-LSTM model is verified using the operation data from an LM2500+ gas turbine. The comparison results demonstrate the superiority of the proposed method over traditional architectures. The CAPI-LSTM model achieves the lowest root mean square error of 0.177 MW, and its error distribution is the most concentrated near zero among all compared models. The robustness of the CAPI-LSTM model is further verified under the unseen operation conditions. The CAPI-LSTM still maintains excellent generalization capability compared to both purely data-driven models and standard physics-informed models, with an average error of only 0.218 MW and a narrow interquartile range of [0.058, 0.363]. The paired t-test results confirm that the improvement of the CAPI-LSTM model is statistically significant. The CAPI-LSTM model achieves competitive computational efficiency despite the integration of the physics-informed loss function with a condition-adaptive regulator. Furthermore, the CAPI-LSTM model achieves superior performance in noise immunity and transferability to other types of gas turbines. In summary, the proposed CAPI-LSTM model provides an effective and practical solution for marine gas turbine output power prediction. Full article

Reactive power compensation (RPC) in ships is critical for the stability, efficiency, and safety of the electric power system. It ensures voltage stability, reduces generator and alternator load, and adapts to dynamic and variable loads. This study evaluates the ship’s electrical power system. The implementation of advanced compensation strategies across three distinct operational scenarios is intended to systematically mitigate reactive load demand, thereby contributing to the enhancement of overall power utilization efficiency and ensuring improved stability of the vessel’s energy management framework. Three real-life data sets (148, 120, and 102 min) were analyzed to extract reactive power variations. MATLAB R2023b is used to calculate and graph required compensation and capacitance, generated time-series responses, and produced comparative graphs, enabling evaluation of the most effective compensation strategy for a shipboard microgrid in diesel–electric supply vessel systems. The findings highlight the importance of advanced control algorithms, predictive management, and hybrid compensation topologies in achieving reliable and efficient reactive power management in ships. There were three distinct situations in which the goal power factor (PF) values of 0.90, 0.95, and 0.98 were analyzed. These values were determined under three load conditions. Within the context of operations, compensating just up to 0.90 resulted in savings of 6.26%; however, optimizing up to 0.98 resulted in an increase in savings to 13.91%, which is over double the amount. Full article

In response to the International Maritime Organization’s emission reduction targets, ship power systems are transitioning toward microgrid architectures with high renewable energy penetration. In islanded mode, the lack of main grid support and the low inertia of power electronic interfaces pose significant frequency stability challenges. Virtual Synchronous Generator (VSG) technology offers an effective solution, but conventional VSG control exhibits two inherent limitations: steady-state frequency deviation under load variations due to its primary regulation nature, and poor dynamic response characterized by large overshoot and prolonged settling time. This paper proposes an enhanced VSG control strategy integrating two key innovations: (i) a communication-free secondary frequency regulation loop that eliminates steady-state error, and (ii) an adaptive control scheme for virtual inertia and damping coefficients that dynamically responds to frequency deviations and their rate of change. The adaptive mechanism reduces overshoot by 57% (from 0.14 Hz to 0.06 Hz) and shortens settling time by 40% (from 0.38 s to 0.23 s) compared to non-adaptive secondary regulation, as demonstrated through MATLAB/Simulink simulations and 6 kW experimental prototype validation. The proposed strategy ensures both steady-state accuracy and enhanced transient performance, providing a reliable solution for improving power quality in islanded shipboard microgrids and contributing to maritime decarbonization goals. Full article

In this paper, frequency control of shipboard microgrids is achieved in the presence of measurement noise, dynamic uncertainty, and actuator faults. Measurement noise arises from incorrect signal processing, electromagnetic interference, converter switching dynamics, mechanical vibrations from propulsion and generators, and transients caused by sudden changes in load or generation. Actuator faults are caused by intense mechanical vibrations, temperature-induced stress, degradation of power electronic devices, communication latency, and wear or saturation in fuel injection and governor components. To regulate the frequency deviation under these challenges, a cross-entropy-based fault-tolerant model predictive control method, utilizing a discrete-time linear Kalman filter, is developed. Firstly, the discrete-time linear Kalman filter ensures that uncertain states of the shipboard microgrids are measurable in a noisy environment. Afterward, the model predictive control scheme is employed to obtain an optimal control input based on the measurable states. This controller ensures the frequency regulation of shipboard microgrids in the presence of measurement noise. Furthermore, a fault-tolerant control technique that utilizes the concept of cross-entropy is extended to provide a robust controller that verifies the frequency regulation of shipboard microgrids with actuator faults. To demonstrate the stability of the closed-loop system of the shipboard microgrids based on the proposed controller, considering the effects of measurement noise, state uncertainty, and actuator faults, the Lyapunov stability concept is employed. Finally, simulation results in MATLAB/Simulink R2025b are provided to show that the proposed control method for frequency regulation in renewable shipboard microgrids is both effective and practicable. Full article

The complex topology of shipboard DC microgrids and the strong coupling between positive and negative poles during faults pose significant challenges for faulted-pole identification, especially under high-resistance conditions. To address these issues, this paper proposes a novel faulted-pole identification method based on S-Transformation and convolutional neural networks (CNNs). Single-ended voltage and current measurements from the generator side are used to generate time–frequency spectrograms via S-Transformation, which are then processed by a CNN trained to classify the faulted pole. This approach avoids reliance on complex threshold settings. Simulation results on a representative shipboard DC microgrid demonstrate that the proposed method achieves high accuracy, fast response, and strong robustness, even under high-resistance fault scenarios. The method significantly enhances the selectivity and reliability of fault protection, offering a promising solution for advanced marine DC power systems. Compared to conventional fault-diagnosis techniques, the proposed model achieves notable improvements in classification accuracy and computational efficiency for line-fault detection. Full article

The growing demand for low-emission maritime transport and efficient onboard energy management has intensified research into advanced control strategies for hybrid shipboard microgrids. These systems integrate both AC and DC power domains, incorporating renewable energy sources and battery storage to enhance fuel efficiency, reduce greenhouse gas emissions, and support operational flexibility. However, integrating renewable energy into shipboard microgrids introduces challenges, such as power fluctuations, varying line impedances, and disturbances caused by AC/DC load transitions, harmonics, and mismatches in demand and supply. These issues impact system stability and the seamless coordination of multiple distributed generators. To address these challenges, we proposed a hierarchical control strategy that supports sustainable operation by improving the voltage and frequency regulation under dynamic conditions, as demonstrated through both MATLAB/Simulink simulations and real-time hardware validation. Simulation results show that the proposed controller reduces the frequency deviation by up to 25.5% and power variation improved by 20.1% compared with conventional PI-based secondary control during load transition scenarios. Hardware implementation on the NVIDIA Jetson Nano confirms real-time feasibility, maintaining power and frequency tracking errors below 5% under dynamic loading. A comparative analysis of the classical PI and sliding mode control-based designs is conducted under various grid conditions, such as cold ironing mode of the shipboard microgrid, and load variations, considering both the AC and DC loads. The system stability and control law formulation are verified through simulations in MATLAB/SIMULINK and practical implementation. The experimental results demonstrate that the proposed secondary control architecture enhances the system robustness and ensures sustainable operation, making it a viable solution for modern shipboard microgrids transitioning towards green energy. Full article

Gas turbines in land-based microgrids and shipboard-isolated power grids frequently face operational challenges, such as the startup and shutdown of high-power equipment and sudden load fluctuations, which significantly impact their performance. To examine the dynamic behavior of gas turbines under transitional operating conditions, a three-dimensional computational fluid dynamic simulation is employed to create a model of the gas turbine rotor, incorporating thermal inertia, which is then analyzed in conjunction with three-dimensional finite element methods. The governing equations of the flow field are discretized, providing results for the flow and temperature fields throughout the entire flow path. A hybrid approach, combining temperature differences and heat flux density, is applied to set the thermal boundary conditions for the walls, with the turbine’s operational state determined based on the direction of heat transfer. Additionally, mesh division techniques and turbulence models are selected based on the geometric dimensions and operating conditions of the compressor and turbine. The simulation results reveal that thermal inertia induces a shift in the dynamic characteristics of the rotor components. Under the same heat transfer conditions, variations in rotational speed have a minimal impact on the shift in the characteristic curve. The working fluid temperature inside the compressor components is lower, with a smaller temperature difference from the wall, resulting in less intense heat transfer compared to the turbine components. Overall, heat transfer accounts for only about 0.1% of the total enthalpy at the inlet. When heat exchange occurs between the working fluid and the walls, around 6–15% of the exchanged heat is converted into changes in technical work, with this percentage increasing as the temperature difference rises. Full article

Reactive power sharing in distributed generators (DGs) is one of the key issues in the control technologies of greenship microgrids. Reactive power imbalance in ship microgrids can cause instability and potential equipment damage. In order to improve the poor performance of the traditional adaptive droop control methods used in microgrids under high-load conditions and influenced by virtual impedance parameters, this paper proposes a novel strategy based on the deep reinforcement learning DQN-VI, in which a deep Q network (DQN) is combined with the virtual impedance (VI) method. Unlike traditional methods which may use static or heuristically adjusted VI parameters, the DQN-VI strategy employs deep reinforcement learning to dynamically optimize these parameters, enhancing the microgrid’s performance under varying conditions. The proposed DQN-VI strategy considers the current situation in greenships, wherein microgrids are generally equipped with cables of different lengths and measuring the impedance of each cable is challenging due to the lack of space. By modeling the control process as a Markov decision process, the observation space, action space, and reward function are designed. In addition, a deep neural network is used to estimate the Q function that describes the relationship between the state and the action. During the training of the DQN agent, the process is optimized step-by-step by observing the state and rewards of the system, thereby effectively improving the performance of the microgrids. The comparative simulation experiments verify the effectiveness and superiority of the proposed strategy. Full article

For the frequent occurrence of pulse power load operation and load switching disturbances in AC/DC shipboard microgrids, a large-signal stability analysis method based on hybrid potential theory is proposed. The proposed method utilizes a mixed potential function to analyze the impact of interconnected converters on system stability. First, the entire system is equivalently modeled as a DC system in a d-q rotating reference frame. Then, a mixed potential function model of the AC/DC system is established for stability analysis, leading to the development of a large-signal stability criterion for the system. Using this criterion, the boundary values of bidirectional power transfer for the interconnected converters are derived. Finally, a simulation model of the AC/DC hybrid microgrid system was built in Simulink for verification, and further validation was carried out on the RT-lab hardware-in-the-loop (HIL) simulation platform. Simulation and experimental results show that the proposed criterion can effectively ensure the stability of the AC/DC hybrid microgrid system under large disturbances. Full article

This paper investigates the secondary control problem of shipboard microgrids (SMGs) with a high percentage of new energy sources under general noise. Firstly, a polymorphic SMG model is constructed, which enables the software-defined functionality of the control strategy and allows heterogeneous distributed generators (DGs) in AC SMGs to exchange packets of different types. Secondly, due to the presence of highly dynamic and high-power loads in the SMGs, a containment-based distributed secondary control strategy is proposed to improve the flexibility of the DG voltage regulation. Then, considering the complexity and diversity of disturbances during ship navigation, general noise is introduced instead of white noise to describe various disturbances. Furthermore, based on the random differential equations (RDEs), the NOS stability of the proposed strategy is proved using Lyapunov theory, which proves the effectiveness of the containment-based distributed secondary control strategy under general noise. And, the containment error is obtained to prove that the voltage and frequency of the system converge to the convex hull spanned by the virtual leaders, ensuring the high quality of the power supply. Finally, the validity of the proposed containment-based strategy is verified by an AC SMG model with four DGs in three cases. Full article

State-of-charge (SoC) consistency and bus voltage regulation are two major control objectives of shipboard DC microgrids. To achieve these objectives, this paper presents a novel distributed model predictive control (DMPC) strategy with multiple cost functions. Firstly, based on the bus voltage derivative and SoC dynamic model, the voltage and SoC control equations in discrete form are established. Subsequently, considering the safe operation of battery energy storage systems (BESSs), a DMPC taking the energy storage current as the control set is designed. Finally, the average voltage compensation is taken to achieve precise control of the average voltage. The proposed method can avoid the complex process of adjusting weight coefficients, thereby simplifying controller design. Furthermore, the robustness and practicality of the proposed DMPC method are verified through MATLAB/Simulink 2021a simulations and hardware-in-the-loop (HiL) experiments. Full article