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Powering the Seas: Revolutionizing Shipboard Power Systems with Advanced Control,...
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Powering the Seas: Revolutionizing Shipboard Power Systems with Advanced Control, Alternative Fuels, and Renewable Energy

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: closed (5 November 2024) | Viewed by 6283

Special Issue Editors

Dr. Jian Shi

E-Mail Website
Guest Editor
Department of Engineering Technology, University of Houston, Houston, TX 77004, USA
Interests: power and energy systems; transportation electrification; deep decarbonization; equity in energy transition; cyber-security of the power grid
Special Issues, Collections and Topics in MDPI journals
Dr. Bowen Xing

E-Mail Website
Guest Editor
College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China
Interests: unmanned surface vehicles; ship motion control; path planning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gino Lim

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Houston, Houston, TX 77001, USA
Interests: developing optimization techniques (machine learning, robust optimization and stochastic programming) for solving large-scale decision-making problems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on “Powering the Seas: Revolutionizing Shipboard Power Systems with Advanced Control, Alternative Fuels, and Renewable Energy”. Facing the challenges of global climate change, the maritime industry has placed greater emphasis on the development and integration of energy-efficient ship power systems, while adopting advanced control strategies to enhance sustainability and performance.

The Special Issue is dedicated to exploring the integration of advanced control techniques, the application of alternative fuels, and the implementation of renewable energy technologies in the maritime field. The submission of high-quality technical papers is particularly welcome, and potential topics include, but are not limited to, the following:

  • Advanced control technologies in shipboard power systems;
  • Mitigation instabilities in marine vessels vis advanced control techniques;
  • Stability analysis and control of shipboard power systems;
  • Potential of biofuels in decarbonizing the maritime Industry;
  • Wave Energy Conversion for Maritime Propulsion;
  • Integration of renewable energy sources in marine propulsion systems;
  • Energy management strategies for marine renewable energy systems;
  • Advanced energy storage systems for marine vehicles;
  • Energy harvesting in marine vehicles.

This Special Issue aims to advance the maritime industry towards the attainment of green and efficient solutions to the challenges of climate change via the performance of in-depth research and the creation of cutting-edge technologies in the related fields.

Dr. Jian Shi
Dr. Bowen Xing
Prof. Dr. Gino Lim
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • shipboard power systems
  • advanced control
  • alternative fuels
  • renewable energy
  • maritime applications
  • energy efficiency
  • resilience
  • stability
  • biofuels
  • liquified natural gas
  • hydrogen fuel
  • solar power
  • wind power
  • wave energy

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

27 pages, 7276 KiB  
Article
Advanced Design of Naval Ship Propulsion Systems Utilizing Battery-Diesel Generator Hybrid Electric Propulsion Systems
by Youngnam Park and Heemoon Kim
J. Mar. Sci. Eng. 2024, 12(11), 2034; https://doi.org/10.3390/jmse12112034 - 10 Nov 2024
Viewed by 651
Abstract
As advanced sensors and weapons require high power, naval vessels have increasingly adopted electric propulsion systems. This study aims to enhance the efficiency and operability of electric propulsion systems over traditional mechanical propulsion systems by analyzing the operational profiles of modern naval vessels. [...] Read more.
As advanced sensors and weapons require high power, naval vessels have increasingly adopted electric propulsion systems. This study aims to enhance the efficiency and operability of electric propulsion systems over traditional mechanical propulsion systems by analyzing the operational profiles of modern naval vessels. Consequently, a battery-integrated generator-based electric propulsion system was selected. Considering the purpose of the vessel, a specification selection procedure was developed, leading to the design of a hybrid electric propulsion system (comprising one battery and four generators). The power management control technique of the proposed propulsion system sets the operating modes (depending on the specific fuel oil consumption of the generators) to minimize fuel consumption based on the operating load. Additionally, load distribution control rules for the generators were designed to reduce energy consumption based on the load and battery state of charge. MATLAB/Simulink was used to evaluate the proposed system, with simulation results demonstrating that it maintained the same propulsion performance as existing systems while achieving a 12-ton (22%) reduction in fuel consumption. This improvement results in cost savings and reduced carbon dioxide emissions. These findings suggest that an efficient load-sharing controller can be implemented for various vessels equipped with electric propulsion systems, tailored to their operational profiles. Full article
(This article belongs to the Special Issue Powering the Seas: Revolutionizing Shipboard Power Systems with Advanced Control, Alternative Fuels, and Renewable Energy)
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32 pages, 8805 KiB  
Article
The Application of an Improved LESS Dung Beetle Optimization in the Intelligent Topological Reconfiguration of ShipPower Systems
by Yinchao Tan, Sheng Liu, Lanyong Zhang, Jian Song and Yuanjie Ren
J. Mar. Sci. Eng. 2024, 12(10), 1843; https://doi.org/10.3390/jmse12101843 - 15 Oct 2024
Viewed by 697
Abstract
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. [...] Read more.
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. Full article
(This article belongs to the Special Issue Powering the Seas: Revolutionizing Shipboard Power Systems with Advanced Control, Alternative Fuels, and Renewable Energy)
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19 pages, 6650 KiB  
Article
Stability Analysis in a Direct-Current Shipboard Power System with Parallel Permanent Magnet Synchronous Generators and Supercapacitor Integration
by Qinsheng Yun, Xiangjun Wang, Shenghan Wang, Wei Zhuang and Wanlu Zhu
J. Mar. Sci. Eng. 2024, 12(4), 578; https://doi.org/10.3390/jmse12040578 - 29 Mar 2024
Viewed by 846
Abstract
This paper investigates the small-signal stability of a DC shipboard power system (SPS) with the integration of a supercapacitor. As an efficient energy storage solution, supercapacitors can not only provide rapid energy response to sudden power demand spikes, effectively mitigating load fluctuations, but [...] Read more.
This paper investigates the small-signal stability of a DC shipboard power system (SPS) with the integration of a supercapacitor. As an efficient energy storage solution, supercapacitors can not only provide rapid energy response to sudden power demand spikes, effectively mitigating load fluctuations, but also enhance the system’s resilience to disturbances. In the context of the parallel operation of two Permanent Magnet Synchronous Generators (PMSGs), the inclusion of supercapacitors may alter the system’s dynamic behaviors, thereby affecting its small-signal stability. This paper develops the small-signal model of SPS and explores the small-signal model under various power distribution strategies in the parallel operation of diesel generator sets. Through the calculation of eigenvalues and influence factors, the system’s oscillation modes are analyzed, and key parameters affecting the stability of the DC distribution system are identified. Furthermore, this paper meticulously examines the specific impacts of electrical and control parameter variations on the system’s small-signal stability. Simulation experiments validate the accuracy of the small-signal stability analysis after supercapacitor integration into SPS. Full article
(This article belongs to the Special Issue Powering the Seas: Revolutionizing Shipboard Power Systems with Advanced Control, Alternative Fuels, and Renewable Energy)
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22 pages, 3109 KiB  
Article
Advanced State Estimation Approach for Partially Observable Shipboard Power Systems
by Wanlu Zhu, Tianwen Gu, Jie Wu and Zhengzhuo Liang
J. Mar. Sci. Eng. 2023, 11(12), 2380; https://doi.org/10.3390/jmse11122380 - 18 Dec 2023
Cited by 1 | Viewed by 1057
Abstract
In instances where vessels encounter impacts or other factors leading to communication impairments, the status of electrical equipment becomes inaccessible through standard communication lines for the controllers. Consequently, the shipboard power system enters the partial observable state. Failure to timely ascertain and respond [...] Read more.
In instances where vessels encounter impacts or other factors leading to communication impairments, the status of electrical equipment becomes inaccessible through standard communication lines for the controllers. Consequently, the shipboard power system enters the partial observable state. Failure to timely ascertain and respond to the current state of the shipboard power system with appropriate restorative controls can result in irreversible damages to the electrical infrastructure and potentially precipitate a complete systemic failure. In this paper, an innovative fault-tolerant control and state estimation approach is proposed to address the partial observability problem of shipboard power systems, based on distributed control architecture and hybrid automata modeling, where controllers are unable to fully acquire equipment status due to device failures like sensor malfunctions. This approach infers the overall state of subsystems using data from intact equipment and discrete events from circuit breakers. Through fault-tolerant control techniques, it ensures that the subsystem state avoids invalid regions, effectively preventing the system from entering unhealthy operational states and significantly reducing the risk of performance degradation or systemic collapse due to faults. Simulation results confirm that this approach can quickly and accurately estimate the system’s current state under partial observation, enabling subsequent fault recovery strategies to accurately pinpoint fault locations and identify optimal recovery solutions. Full article
(This article belongs to the Special Issue Powering the Seas: Revolutionizing Shipboard Power Systems with Advanced Control, Alternative Fuels, and Renewable Energy)
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27 pages, 19938 KiB  
Article
Diagnostic Method for Short Circuit Faults at the Generator End of Ship Power Systems Based on MWDN and Deep-Gated RNN-FCN
by Lanyong Zhang, Ziqi Zhang and Huimin Peng
J. Mar. Sci. Eng. 2023, 11(9), 1806; https://doi.org/10.3390/jmse11091806 - 16 Sep 2023
Cited by 3 | Viewed by 1478
Abstract
Synchronous generators with three phases are crucial components of modern integrated power systems in ships. These generators provide power for the entire operation of the vessel. Therefore, it is of paramount importance to diagnose short-circuit faults at the generator terminal in the ship’s [...] Read more.
Synchronous generators with three phases are crucial components of modern integrated power systems in ships. These generators provide power for the entire operation of the vessel. Therefore, it is of paramount importance to diagnose short-circuit faults at the generator terminal in the ship’s power system to ensure the safe and stable operation of modern ships. In this study, a generator terminal short-circuit fault diagnosis method is proposed based on a hybrid model that combines the Multi-Level Wavelet Decomposition Network, Deep-Gated Recurrent Neural Network, and Fully Convolutional Network. Firstly, the Multi-Level Wavelet Decomposition Network is used to decompose and denoise the collected electrical signals, thus dividing them into sub-signals and extracting their time-domain and frequency-domain features. Secondly, synthetic oversampling based on Gaussian random variables is employed to address the problem of imbalance between normal data and fault data, resulting in a balanced dataset. Finally, the dataset is fed into the hybrid model of the Deep-Gated Recurrent Neural Network and Fully Convolutional Network for feature extraction and classification of faults, ultimately outputting the fault diagnosis results. To validate the performance of the proposed method, simulations and comparative analysis with other algorithms are conducted on the fault diagnosis method. The proposed algorithm’s accuracy reaches 96.82%, precision reaches 97.35%, and the area under curve reaches 0.85, indicating accurate feature extraction and classification for identifying short-circuit faults at the generator terminals. Full article
(This article belongs to the Special Issue Powering the Seas: Revolutionizing Shipboard Power Systems with Advanced Control, Alternative Fuels, and Renewable Energy)
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