Special Issue "Renewable Energy Sources for Electrical Power: Reliability Assessment, Condition Monitoring, Prognostics and Health Management, Production Prediction"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Sameer Al-Dahidi
E-Mail Website
Guest Editor
Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman 11180, Jordan
Interests: Prognostics and Health Management, Predictive Maintenance; RAMS; Artificial Intelligence, Machine Learning, Data Mining, Optimization; Mathematical Modelling; Renewable Energy; Wind and Solar Photovoltaic Systems; Energy Forecasting; Performance Analysis; Mechanical Engineering
Dr. Mohamed Louzazni
E-Mail Website
Guest Editor
National School of Applied Science, Abdelmalek Essaâdi University, Tetouan B.P. 2117, Morocco
Interests: Performance analysis; Monitoring; Lifetime Analysis; Fault Detection; Control Management; Hybrid Renewable Energy; Mathematical Modelling; Optimization and Meta-heuristic Algorithm; Computational Intelligence; Photovoltaic & Power Energy; Forecasting; Fuel Cell; Radar; Radio Frequency; Electromagnetic and Electronic
Prof. Dr. Enrico Zio
E-Mail Website
Guest Editor
CentraleSupelec, Laboratoire Genie Industriel, Chair Syst Sci & Energetic Challenge, Fondation EDF, F-91190 Gif Sur Yvette, France and Politecnico di Milano, Energy Department, 20156 Milano, Italy
Interests: characterization and modeling of the failure/repair/maintenance behavior of components, complex energy systems and critical infrastructures like power grids, for the study of their reliability, availability, maintainability, prognostics, safety, vulnerability and resilience, mostly using a computational approach based on advanced Monte Carlo simulation methods, soft computing techniques and optimization heuristics
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Special Issue Information

Dear Colleagues,

Renewable energy sources for electric power generation have been rapidly developing. Their integration into smart power grids aims at targets of energy efficiency with low-carbon operations and energy security. Significant challenges occur in the integration of renewable energy sources at both high and low voltage levels. Additionally, various conditions are required to ensure the safety and reliability of electric power systems based on renewable sources, including wind turbines, photovoltaic power systems, and so on. In this regard, reliability assessment, advanced condition monitoring, early fault detection, fault diagnostics, fault prognostics, system health management, and accurate production prediction under variable weather conditions are of paramount importance.

This Special Issue aims to encourage researchers and practitioners to share and exchange their original and high-quality articles (new theories, methods, techniques, and applications) in the fields of electrical power generation, transmission and distribution, and the integration of renewable energy systems as related to the aforementioned topics. In particular, potential topics include but are not limited to: reliability analysis; reliability testing and statistics; advanced condition monitoring; fault tolerance control; advanced fault detection, diagnostics, and prognostics; prognostics and health management; degradation modeling and analysis; failure mechanisms; short, intermediate, and long-term power production prediction; and embedded systems and the Internet of Things (IoT). The submitted manuscripts for this Special Issue will be peer-reviewed before publication.

Dr. Sameer Al-Dahidi
Dr. Mohamed Louzazni
Prof. Dr. Enrico Zio
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 papers will be 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. Sustainability 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 1900 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

  • renewable energy systems
  • performance analysis
  • reliability assessment
  • optimization
  • condition monitoring
  • prognostics and health management
  • production prediction

Published Papers (1 paper)

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Research

Article
Bootstrapped Ensemble of Artificial Neural Networks Technique for Quantifying Uncertainty in Prediction of Wind Energy Production
Sustainability 2021, 13(11), 6417; https://doi.org/10.3390/su13116417 - 04 Jun 2021
Viewed by 637
Abstract
The accurate prediction of wind energy production is crucial for an affordable and reliable power supply to consumers. Prediction models are used as decision-aid tools for electric grid operators to dynamically balance the energy production provided by a pool of diverse sources in [...] Read more.
The accurate prediction of wind energy production is crucial for an affordable and reliable power supply to consumers. Prediction models are used as decision-aid tools for electric grid operators to dynamically balance the energy production provided by a pool of diverse sources in the energy mix. However, different sources of uncertainty affect the predictions, providing the decision-makers with non-accurate and possibly misleading information for grid operation. In this regard, this work aims to quantify the possible sources of uncertainty that affect the predictions of wind energy production provided by an ensemble of Artificial Neural Network (ANN) models. The proposed Bootstrap (BS) technique for uncertainty quantification relies on estimating Prediction Intervals (PIs) for a predefined confidence level. The capability of the proposed BS technique is verified, considering a 34 MW wind plant located in Italy. The obtained results show that the BS technique provides a more satisfactory quantification of the uncertainty of wind energy predictions than that of a technique adopted by the wind plant owner and the Mean-Variance Estimation (MVE) technique of literature. The PIs obtained by the BS technique are also analyzed in terms of different weather conditions experienced by the wind plant and time horizons of prediction. Full article
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