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Artificial Intelligence in Prognostics and Health Management of Renewable Energy...
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Artificial Intelligence in Prognostics and Health Management of Renewable Energy System

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 3779

Special Issue Editors

Prof. Dr. Daming Zhou

E-Mail Website
Guest Editor
School of Astronautics, Northwestern Polytechnical University, **'an 710072, China
Interests: prognostics and health management in renewable energy system; energy management strategy; fuel cell system modeling, diagnostic and prognostic
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ahmed Al-Durra

E-Mail Website
Guest Editor
Advanced Power & Energy Center, EECS Department, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
Interests: estimation and control systems; renewable energy systems; optimization
Dr. Yongliang Qiao

E-Mail Website
Guest Editor
Australian Centre for Field Robotics (ACFR), University of Sydney, Sydney, NSW 2006, Australia
Interests: field robotics; intelligent perception; visual localization; artificial intelligence; image processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to environmental pollution and the global energy crisis, renewable energy technologies are gaining increasing attention. As a result, complex hybrid power generation systems, including wind, solar, and hydrogen powers, have been developed and optimized in order to ensure the energy security of a country and meet the energy requirements.

However, mass commercial development of the renewable energy is strongly dependent on their operation and maintenance, and there is increasing pressure on industries to move towards more predictive and proactive maintenance practices. To this aim, efforts are being made to develop prognostics and health management (PHM) techniques to provide high-accuracy fault diagnosis and prognostic technologies.

Currently, with advances in sensor technology and signal processing, artificial intelligence (AI) techniques are develo** rapidly and being applied to improve accuracy and efficiency of PHM system, for example the deep learning (DL) and support vector machine (SVM) are being used to increase the overall efficiency of prognostic maintenance systems.

The objective of this Special Issue is to provide a forum for researchers and engineers to report their latest developments and advances of artificial intelligence techniques in PHM of renewable energy system, in order to early detect equipment faults, effectively monitor the health of the production, determine causes of downtime, and shorten the maintenance time and operation cost.

Potential topics include, but are not limited to:

  • Data collection, processing, training, validation, and maintenance decision-making in PHM;
  • Emerging algorithms and techniques of artificial intelligence in PHM;
  • Monitoring, fault diagnosis, and remaining useful life time prediction;
  • Prognostic, health monitoring, and management of renewable energy;
  • Hybrid/fusion approaches combine different data-driven methods in PHM;
  • Decisions condition-based maintenance related to safety, ensuring adequate inventory and product life extension.

Prof. Dr. Daming Zhou
Prof. Dr. Ahmed Al-Durra
Dr. Yongliang Qiao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net 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. Energies is an international peer-reviewed open access semimonthly 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

  • artificial intelligence algorithms
  • prognostics and health management
  • renewable energy system
  • signal processing
  • deep learning
  • artificial neural network
  • condition-based maintenance

Published Papers (3 papers)

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Research

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28 pages, 5014 KiB  
Article
Optimum Design of a Reusable Spacecraft Launch System Using Electromagnetic Energy: An Artificial Intelligence GSO Algorithm
by Huayu Gao, Zheng Wei, **ang Zhang, Pei Wang, Yuwei Lei, Hui Fu and Daming Zhou
Energies 2023, 16(23), 7717; https://doi.org/10.3390/en16237717 - 22 Nov 2023
Viewed by 780
Abstract
Due to its advantages of high acceleration, reusability, environmental protection, safety, energy conservation, and efficiency, electromagnetic energy has been considered as an inevitable choice for future space launch technology. This paper proposes a novel three-level orbital launch approach based on a combination of [...] Read more.
Due to its advantages of high acceleration, reusability, environmental protection, safety, energy conservation, and efficiency, electromagnetic energy has been considered as an inevitable choice for future space launch technology. This paper proposes a novel three-level orbital launch approach based on a combination of a traditional two-level orbital launch method and an electromagnetic boost (EMB), in which the traditional two-level orbital launch consists of a turbine-based combined cycle (TBCC) and a reusable rocket (RR). Firstly, a mathematical model of a multi-stage coil electromagnetic boost system is established to develop the proposed three-level EMB-TBCC-RR orbital launch approach, achieving a horizontal take-off–horizontal landing (HTHL) reusable launch. In order to optimize the fuel quality of the energy system, an artificial intelligence algorithm parameters-sensitivity-based adaptive quantum-inspired glowworm swarm optimization (AQGSO)is proposed to improve the performance of the electromagnetic boosting system. Simulation results show that the proposed AQGSO improves the global optimization precision and convergence speed. By using the proposed EMB-TBCC-RR orbital launch system and the optimization approach, the required fuel weight was reduced by about 13 tons for the same launch mission, and the energy efficiency and reusability of the spacecraft was greatly improved. The spacecraft can be launched with more cargo capacity and increased payload. The proposed novel three-level orbital launch approach can help engineers to design and optimize the orbital launch system in the field of electromagnetic energy conversion and management. Full article
(This article belongs to the Special Issue Artificial Intelligence in Prognostics and Health Management of Renewable Energy System)
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17 pages, 3507 KiB  
Article
Design of a New Single-Cell Flow Field Based on the Multi-Physical Coupling Simulation for PEMFC Durability
by Yuting Zou, Shiyang Hua, Hao Wu, Chen Chen, Zheng Wei, Zhizhong Hu, Yuwei Lei, **hui Wang and Daming Zhou
Energies 2023, 16(16), 5932; https://doi.org/10.3390/en16165932 - 10 Aug 2023
Cited by 2 | Viewed by 1116
Abstract
The fuel cell with a ten-channel serpentine flow field has a low operating pressure drop, which is conducive to extended test operations and stable use. According to numerical results of the ten-channel serpentine flow field fuel cell, the multi-channel flow field usually has [...] Read more.
The fuel cell with a ten-channel serpentine flow field has a low operating pressure drop, which is conducive to extended test operations and stable use. According to numerical results of the ten-channel serpentine flow field fuel cell, the multi-channel flow field usually has poor mass transmission under the ribs, and the lower pressure drop is not favorable for drainage from the outlet. In this paper, an optimized flow field is developed to address these two disadvantages of the ten-channel fuel cell. As per numerical simulation, the optimized flow field improves the gas distribution in the reaction area, increases the gas flow between the adjacent ribs, improves the performance of PEMFC, and enhances the drainage effect. The optimized flow field can enhance water pipe performance, increase fuel cell durability, and decelerate aging rates. According to further experimental tests, the performance of the optimized flow field fuel cell was better than that of the ten-channel serpentine flow field at high current density, and the reflux design requires sufficient gas flow to ensure the full play of the superior performance. Full article
(This article belongs to the Special Issue Artificial Intelligence in Prognostics and Health Management of Renewable Energy System)
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Review

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21 pages, 2030 KiB  
Review
Research Progress on Aging Prediction Methods for Fuel Cells: Mechanism, Methods, and Evaluation Criteria
by Zhuang Tian, Zheng Wei, **hui Wang, Yinxiang Wang, Yuwei Lei, ** Hu, S. M. Muyeen and Daming Zhou
Energies 2023, 16(23), 7750; https://doi.org/10.3390/en16237750 - 24 Nov 2023
Cited by 2 | Viewed by 1395
Abstract
Due to the non-renewable nature and pollution associated with fossil fuels, there is widespread research into alternative energy sources. As a novel energy device, a proton exchange membrane fuel cell (PEMFC) is considered a promising candidate for transportation due to its advantages, including [...] Read more.
Due to the non-renewable nature and pollution associated with fossil fuels, there is widespread research into alternative energy sources. As a novel energy device, a proton exchange membrane fuel cell (PEMFC) is considered a promising candidate for transportation due to its advantages, including zero carbon emissions, low noise, and high energy density. However, the commercialization of fuel cells faces a significant challenge related to aging and performance degradation during operation. In order to comprehensively address the issue of fuel cell aging and performance decline, this paper provides a detailed review of aging mechanisms and influencing factors from the perspectives of both the PEMFC system and the stack. On this basis, this paper offers targeted solutions to degradation issues stemming from various aging factors and presents research on aging prediction methods to proactively mitigate aging-related problems. Furthermore, to enhance prediction accuracy, this paper categorizes and analyzes the degradation index and accuracy evaluation criteria commonly employed in the existing fuel cell aging research. The results indicate that specific factors leading to aging-related failures are often addressed via targeted solving methods, corresponding to specific degradation indexes. The significance of this study lies in the following aspects: (1) investigating the aging factors in fuel cells and elucidating the multiple aging mechanisms occurring within fuel cells; (2) proposing preventive measures, solutions, and aging prediction methods tailored to address fuel cell aging issues comprehensively, thereby mitigating potential harm; and (3) summarizing the degradation index and accuracy evaluation standards for aging prediction, offering new perspectives for resolving fuel cell aging problems. Full article
(This article belongs to the Special Issue Artificial Intelligence in Prognostics and Health Management of Renewable Energy System)
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