Ship Power System Research Articles

New Energy Ship Power System

New Energy Ship Power System

ChatEV: Predicting electric vehicle charging demand as natural language processing

The increasing popularity of electric vehicles (EVs) in recent times has introduced considerable load conditions for urban power grids and transportation systems, which highlights the importance of accurately predicting charging demand to enhance charging efficiency. However, current forecasting methods still face challenges in effectively aligning diverse data and generating accurate predictions that can be applied to unseen scenarios. To overcome the challenges, this work introduces a novel perspective: employing large language models (LLMs) as EV charging demand predictors. First, we reformulate the prediction task into a text-to-text format, enabling seamless and effective alignment of various features within a unified language semantic space. Subsequently, we fine-tune a LLM using a meta-learning framework to adapt it specifically for EV charging prediction. Through comprehensive evaluations, it has been demonstrated that the proposed model, ChatEV, achieves outstanding performance in EV charging demand forecasting, particularly in scenarios with limited data.

Ship Power System Network Reconfiguration Based on Swarm Exchange Particle Swarm Optimization Algorithm

As one of the important components of a ship, the ship’s integrated power system is an important safeguard for ships. In order to improve the service life of the ship’s power grid, the power system should be able to realize rapid reconstruction to ensure continuous power supply of important loads when the ship is attacked or fails suddenly. Therefore, it is of vital importance to study the reconfiguration technology of the ship’s integrated power system to ensure that it can quickly and stably cope with all kinds of emergencies in order to guarantee the safe and reliable navigation of the ship. This paper takes the ship’s ring power system as the research object and sets up the maximum recovery load and the minimum number of switching operations. The load is divided uniformly and the generator efficiency is balanced for the reconstruction of comprehensive function. It also sets up the system capacity, topology, and branch current limitations of the constraints to establish a mathematical model. The load branch correlation matrix method is used for branch capacity calculation and generator efficiency equalization calculation, and the load backup power supply path matrix is added on the basis of the matrix to judge the connectivity of some loads before reconfiguration. In this paper, for the network reconfiguration of the ship circular power system, which is a discrete nonlinear problem with multiple objectives, multiple time periods, and multiple constraints, we choose to use the particle swarm algorithm, which is suitable for global optimization, with a simple structure and fewer parameters; improve the particle swarm algorithm using the swarm exchange strategy by setting up two main and auxiliary swarms for global and local search; and exchange some of the particles with the golden ratio in order to keep the diversity of the populations. The simulation results of the network reconfiguration of the ship power system show that the improved algorithm can solve the power system network reconfiguration problem more effectively and provide a feasible reconfiguration scheme in a shorter time compared with the chaotic genetic algorithm under the same fault case test, and it also proves that the use of the swarm exchange particle swarm algorithm greatly improves the performance of reconfiguring the power grid of the ship.

The Application of an Improved LESS Dung Beetle Optimization in the Intelligent Topological Reconfiguration of ShipPower Systems

To address the shortcomings of the Dung Beetle Optimization (DBO) algorithm in ship power-system fault reconfiguration, such as low population diversity and an imbalance between global exploration and local exploitation, the authors of this paper propose an improved Dung Beetle Optimization (LESSDBO) algorithm. The improvements include optimizing the initial population using Latin hypercube sampling and an elite population strategy, optimizing parameters with an improved sigmoid activation function, introducing the sine–cosine algorithm (SCA) for position update optimization, and performing multi-population mutation operations based on individual quality. The LESSDBO algorithm was applied to simulate the fault reconfiguration of a ship power system, and it was compared with the traditional DBO, Genetic Algorithm (GA), and Modified Particle Swarm Optimization (MSCPSO) methods. The simulation results showed that LESSDBO outperformed the other algorithms in terms of convergence accuracy, convergence speed, and global search capability. Specifically, in the reconfiguration under Fault 1, LESSDBO achieved optimal convergence in seven iterations, reducing convergence iterations by more than 30% compared with the other algorithms. In the reconfiguration under Fault 2, LESSDBO achieved optimal convergence in eight iterations, reducing convergence iterations by more than 23% compared with the other algorithms. Additionally, in the reconfiguration under Fault Condition 1, LESSDBO achieved a minimum of four switch actions, which is 33% fewer than the other algorithms, on average. In the reconfiguration under Fault Condition 2, LESSDBO achieved a minimum of eight switch actions, which is a 5.9% reduction compared with the other algorithms. Furthermore, LESSDBO obtained the optimal reconfiguration solution in all 50 trials for both Faults 1 and 2, demonstrating a 100% optimal convergence probability and significantly enhancing the reliability and stability of the algorithm. The proposed method effectively overcomes the limitations of the traditional DBO in fault reconfiguration, providing an efficient and stable solution for the intelligent topology reconfiguration of ship power systems.

Anomaly detection and confidence interval‐based replacement in decay state coefficient of ship power system

AbstractThe anomaly detection and predictive replacement of the degradation decay state coefficient (Desc) of ship power system (SPS) are crucial for ensuring their operational safety and maintenance efficiency. This study introduces the YC3Model, a model based on a dynamic triple sliding window mechanism, and Gaussian process regression) to address this challenge. It combines the temporal variation characteristics of the decay state coefficient's original data, first‐order, and second‐order differential data in both normal and abnormal trend intervals. The model calculates three local statistical measures within each sliding window and employs the Z‐score method for anomaly detection. The combination of three sliding windows reduces false positives and negatives, enhancing the precision of anomaly detection. For detected anomalies, Gaussian process regression is used for prediction and replacement, providing confidence intervals to increase the reliability of the predicted values. Experimental results demonstrate that the YC3Model exhibits superior anomaly detection accuracy and adaptability in the degradation process of SPS, surpassing traditional methods across a range of evaluation metrics. This confirms the potential of YC3Model in health monitoring and predictive maintenance of SPS, offering reliable data input for the intelligent operation and maintenance (IO&M) of SPS.

Fuzzy Logic-Based Energy Management Strategy for Hybrid Fuel Cell Electric Ship Power and Propulsion System

The growing use of proton-exchange membrane fuel cells (PEMFCs) in hybrid propulsion systems is aimed at replacing traditional internal combustion engines and reducing greenhouse gas emissions. Effective power distribution between the fuel cell and the energy storage system (ESS) is crucial and has led to a growing emphasis on developing energy management systems (EMSs) to efficiently implement this integration. To address this goal, this study examines the performance of a fuzzy logic rule-based strategy for a hybrid fuel cell propulsion system in a small hydrogen-powered passenger vessel. The primary objective is to optimize fuel efficiency, with particular attention on reducing hydrogen consumption. The analysis is carried out under typical operating conditions encountered during a river trip. Comparisons between the proposed strategy with other approaches—control based, optimization based, and deterministic rule based—are conducted to verify the effectiveness of the proposed strategy. Simulation results indicated that the EMS based on fuzzy logic mechanisms was the most successful in reducing fuel consumption. The superior performance of this method stems from its ability to adaptively manage power distribution between the fuel cell and energy storage systems.

Advanced Energy Management System for Generator–Battery Hybrid Power System in Ships: A Novel Approach with Optimal Control Algorithms

Advanced Energy Management System for Generator–Battery Hybrid Power System in Ships: A Novel Approach with Optimal Control Algorithms

Design of an FPGA-based short-circuit fault diagnosis algorithm for DC current-limiting fuses

Abstract In recent years, DC current-limiting fuses have played a crucial role in the comprehensive power systems of ships, rail transport, and new energy sectors, ensuring the safe and reliable operation of DC power systems. However, with the increase in the capacity of DC power systems, short-circuit faults can lead to a rise in short-circuit current at rates of up to 40 A/μs, with peak short-circuit currents reaching up to 105KA. This poses higher demands on the accuracy and speed of fuse diagnostics to interrupt short-circuit currents. To address this issue, this paper introduces a fault diagnostic algorithm based on FPGA that can calculate the rise rate of short-circuit currents in real-time: employing FPGA with its high integration, abundant logic resources, and parallel data processing capabilities as the core of the measurement and control system to enhance the speed of short-circuit fault diagnosis from a hardware perspective; replacing the common large current trip protection and DDL protection algorithms with the Newton interpolation algorithm. This improvement calculates the rise rate of each current data point instead of the average rise rate over a period, thus enhancing the speed and accuracy of short-circuit fault diagnosis from a software perspective. The paper also discusses the design of the system’s hardware and software and the construction of the experimental platform. Simulation and experimental results show that the fault diagnosis time of the algorithm has been reduced from 98μs to 36μs, with the numerical error in the short-circuit current rise rate within 2%, and a significant reduction in the peak value of short-circuit current, meeting performance requirements and effectively ensuring the safe and reliable operation of DC power systems.

Solutions for the Electrical Energy Storage System Use in Ship Power Systems

Solutions for the Electrical Energy Storage System Use in Ship Power Systems

Ship propulsion shafting assembly detection: a model-data-driven approach

ABSTRACT The ship propulsion shafting assembly is one of the keys in building a ship power system, which directly affects the power performance and navigation safety. In this paper, a model-data-driven approach called semi-supervised domain-adversarial neural networks (SDANN) is proposed to solve the lack of actual training samples during ship propulsion shafting assembly. In terms of the model, a finite element model (FEM) for ship propulsion shafting considering misalignment is built in ANSYS. In terms of data, a ship propulsion shafting comprehensive test platform is set up in order to collect the actual data in the laboratory. Furthermore, a small amount of the unlabelled actual data is used to train semi-supervised networks, which can identify four different bearing elevations. The research results illustrate that the proposed method outperformed classical deep learning methods in the ship propulsion shafting assembly detection under three types of cases, with an average accuracy of 95.61% ± 0.45%.

Dynamic Fault Reconfiguration of Distribution Networks in Ship Power Systems Based on Deep Reinforcement Learning Approach

Dynamic Fault Reconfiguration of Distribution Networks in Ship Power Systems Based on Deep Reinforcement Learning Approach

A wind-to-wake approach for selecting future marine fuels and powertrains

Global shipping contributes (2.8%) to greenhouse gas emissions and needs to find future fuels which will allow the International Maritime organisation's 2050 net zero targets to be met. There is much uncertainty as to which fuels should be used between hydrogen, ammonia or methanol as future costs are uncertain. We propose and evaluate a Wind-to-Wake ratio that provides an objective measure based on the amount of renewable energy required to propel a ship. Time domain voyage energy demand profiles are used to simulate the fuel used and emissions for a cruise ship, a research survey vessel, a container ship and a large multi-fuel Liquefied Natural Gas carrier. Alternative ship power systems are investigated using various carbon-based fuels, ammonia and hydrogen either using conventional engines or fuel cells. For all voyage and fuel scenarios the combination of hydrogen and fuel cells uses 30% less renewable energy than ammonia and 26% less than methanol. Hydrogen only emits steam whereas the other fuel scenarios still emit significant amounts of greenhouse gasses.

Optimal allocation of distributed energy resources to cater the stochastic E-vehicle loading and natural disruption in low voltage distribution grid

The everyday extreme uncertainties become the new normal for our world. Critical infrastructures like electrical power grid and transportation systems are in dire need of adaptability to dynamic changes. Moreover, stringent policies and strategies towards zero carbon emission require the heavy influx of renewable energy sources (RES) and adoption of electric transportation systems. In addition, the world has seen an increased frequency of extreme natural disasters. These events adversely impact the electrical grid, specifically the less hardened distribution grid. Hence, a resilient electrical network is the demand of the future to fulfill critical loads and charging of emergency electrical vehicles (EV). Therefore, this paper proposes a two-dimensional methodology in planning and operational phase for a resilient electric distribution grid. Initially stochastic modelling of EV load has been performed duly considering the geographical feature and commute pattern to form probability distribution functions. Thenceforth, the impact assessment of extreme natural events like earthquakes using damage state classification has been done to model the impact on distribution grid. The efficacy of the proposed methodology has been tested by simulating an urban Indian distribution grid with mapped EV on DigSILENT PowerFactory integrated with supervised learning tools on Python. Subsequently 24-h load profile before event and after event have been compared to analyze the impact.

Challenges and Solutions of Ship Power System Electrification

Challenges and Solutions of Ship Power System Electrification

Simulation and analysis of short circuit fault in ship power system based on simulink

Abstract In this paper, a short-circuit fault simulation of a ship’s electrical power system is carried out based on Simulink. According to the power system sketch of the ship, a simulation model is built, initial conditions are set, two-phase short-circuit faults and three-phase short-circuit faults are simulated, and the simulation results are analyzed. The results show that the short-circuit fault has a large impact on both the generator side and the load side, in which the impact on the load side is more serious. At the generator side, the voltage of the corresponding phase drops by about 1/3-1/2, and the current increases by about 1.5 times. At the load side, when a two-phase short circuit occurs, the voltage drops to half of the original one, and when a three-phase short circuit occurs, the voltage drops to 0. The short-circuit current has the largest value at the beginning of the short circuit, then oscillates and decays, and finally stabilizes at about 15 times the normal working current.

Research on Big Data Intelligent System for Fault Diagnosis of Computer Aided Ship Power System

This paper aims to build a new fault diagnosis system based on big data and artificial intelligence technology. This study first expounds the application value and prospect of big data technology and intelligent algorithm in the fault diagnosis of ship power system, and realizes the effective integration and mining of massive, multi-source and heterogeneous ship power system operation data through in-depth analysis of the characteristics of ship power system operation data. Secondly, in view of the nonlinear and time-varying characteristics of ship power system faults, this study designs and implements a fault diagnosis model that integrates intelligent algorithms such as machine learning and deep learning. The model can automatically extract key fault features, realize early warning and accurate identification of potential faults, and improve the accuracy and response speed of fault diagnosis through continuous learning and optimization. Finally, this study applies the big data intelligent system for fault diagnosis of computer-aided ship power system to a practical case to verify its effectiveness and practicability in improving fault diagnosis efficiency, reducing maintenance costs, and ensuring navigation safety. The research results have important theoretical significance and practical value for improving the level of ship operation and maintenance management and ensuring the stable operation of ship power system.

Analyzing Power Systems and Power Electronics Interdependencies: A DEMATEL Method Approach

Energy research encompasses all aspects of electrical energy, focusing on innovation in energy production and delivery, alternative resources, and efficient devices. It involves studying systems and equipment for converting, providing, and utilizing energy as electricity. Power electronics have become essential to power systems, enhancing quality and efficiency and promoting the development of intelligent, efficient energy solutions. There are various types of power electronics within power systems. The architectural study of converting electrical energy from one form to another falls under power electronics. Globally, electronics recycle or recover more than 80% of the total electricity produced, averaging 3.4 billion kilowatt-hours annually. Power electronics converters, also known as power converters or switching converters, are used to process or convert electrical energy. Electricity exists as AC power and DC power, leading to the classification of distribution systems into AC and DC based on the type of power used. Power system analysis is crucial for designing electrical power systems. It involves calculations and simulations to ensure that the electrical system and its components are appropriately specified to function as intended, endure expected stress, and be protected from failures. Advantages of power electronics include high power density electricity and improved efficiency, reaching up to 99% in energy conversion. Their efficiency and reliability make switching power supplies suitable for medical devices with acoustically sensitive applications. Power system reliability addresses issues like service interruptions and power outages, often guided by specific codes relevant to consumers. Common reliability indices in the US include SAIFI, SAIDI, and CAIDI. The DEMATEL (Decision Making Trial and Evaluation Laboratory) method is applied across various industries such as non-metal mineral products, general equipment manufacturing, coal mining and washing, textiles, and food manufacturing. DEMATEL visualizes and assesses the interactions and dependent relationships between factors through a structural model, identifying critical elements. Evaluation parameters for power electronics include their application in power systems, transportation systems, energy conservation, heating and lighting control, and renewable energy integration. Power electronics in power systems are ranked highest, while energy conservation is ranked lowest.

A Review of Research on Marine Main Propulsion Systems

Ship power, as an essential component of ship engineering, directly affects the navigation performance, economy, and environmental friendliness of ships. This article reviews the development history of ship power, introduces the main propulsion devices including internal combustion engines, steam engines, and electric motors, and discusses the structure and optimization design of ship power systems. Modern ship power technology includes traditional diesel and gasoline engines, as well as new power devices such as liquefied natural gas (LNG) engines, hybrid power systems, and fuel cells. Electric ships are gaining attention due to their efficiency and environmental benefits. In the future, ship power technology will develop towards green environmental protection, intelligence, integration, diversification, and digitization, providing more reliable and efficient power support for the shipping industry and the advancement of ship engineering.

Efficient and sustainable power propulsion for all-electric ships: An integrated methanol-fueled SOFC-sCO2 system

To address marine pollution caused by fuel usage and reduce carbon emissions in ships, the development of alternative fuel electric propulsion ship power systems presents a promising solution. In this study, an integrated Methanol-fueled SOFC-sCO2 combined power system is proposed for all-electric ships. The system efficiently utilizes waste heat from SOFCs by integrating it into a partial heating sCO2 power cycle to generate additional power. Through the utilization of a thermodynamic model, parametric sensitivity analysis and multi-objective optimization are conducted to maximize the system's energy efficiency while reducing investment and maintenance costs. Comprehensive energy, exergy, and economic analyses are performed on the optimized system. The achieved energy efficiency and exergy efficiency are 66.10 % and 57.74 % respectively, with an investment and maintenance cost of 16.96 $/h and a payback period of 2.2 years. When applied to container ships and compared with other ship power systems, the proposed system demonstrates the highest energy efficiency, delivering an impressive output power of 65,092 kW. The energy efficiency design index (EEDI) of the system is moderate, reaching 5.94. Despite the relatively high fuel cost, the proposed system offers a simpler design compared to existing SOFC-sCO2 systems and is only slightly more complex than integrated systems like SOFC-GT.

Simulation Analysis of a Methanol Fueled Marine Engine for the Ship Decarbonization Assessment

Methanol as marine fuel represents one of the most cost-effective and practical solutions towards low-carbon shipping. Methanol fueled internal combustion engines have a high level of technological readiness and are already available on the market; however, technical data in terms of fuel consumption and emissions are not yet easily accessible. For this reason, the present study deals with the simulation of a virtual spark-ignition methanol engine, carried out in a Matlab-Simulink© R2023a environment to assess the CO2 emissions in several working conditions of a possible ship power system. The thermodynamic model of the methanol fueled engine is derived from a marine gas engine simulator, already validated by the authors in a previous work. This article presents the relevant modifications necessary to adapt the engine to the methanol fuel mode with regard to the different fuel characteristics. The simulation analysis compares the results of the virtual methanol engine with available data from a similar, existing gas engine, highlighting the differences in efficiency and carbon dioxide emissions.