Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.2
Journals
Energies
Special Issues
State-of-the-Art Artificial Intelligence Models for PV Fault Detection
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

State-of-the-Art Artificial Intelligence Models for PV Fault Detection

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "K: State-of-the-Art Energy Related Technologies".

Deadline for manuscript submissions: closed (25 March 2022) | Viewed by 3439

Special Issue Editors

Dr. Mahmoud Dhimish

E-Mail Website
Guest Editor
Department of Electronic Engineering, University of York, Heslington YO10 5DD, UK
Interests: photovoltaics; renewable energy; artificial intelligence; fault detection; power electronics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yihua Hu

E-Mail Website
Guest Editor
Department of Electronic Engineering, University of York, Heslington, York YO10 5DD, UK
Interests: renewable generation; power electronics converters & control; electric vehicle; more electric ship/aircraft; smart energy system and non-destructive test technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Even with the consistent growth in global photovoltaic (PV) capacity, the necessity for fault detection in PV systems has not been widely addressed regardless of its importance. Therefore, this Special Issue aims to solicit original and high-quality research articles related to the aforementioned topics. In particular, topics of interest include but are not limited to: 

  • PV fault detection and classification using mathematical and statistical-based algorithms;
  • PV fault detection and classification using artificial intelligence (AI) models;
  • Degradation estimation of PV systems;
  • On-site characterization and inspection of PV systems (photoluminescence, thermography, electroluminescence).

Other relevant topics will also be considered.

Dr. Mahmoud Dhimish
Prof. Dr. Yihua Hu
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. 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

  • PV fault detection and classification using mathematical and statistical-based algorithms
  • PV fault detection and classification using artificial intelligence (AI) models
  • degradation estimation of PV systems
  • on-site characterization and inspection of PV systems

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Review

24 pages, 5240 KiB  
Review
A Review of Models for Photovoltaic Crack and Hotspot Prediction
by Georgios Goudelis, Pavlos I. Lazaridis and Mahmoud Dhimish
Energies 2022, 15(12), 4303; https://doi.org/10.3390/en15124303 - 12 Jun 2022
Cited by 24 | Viewed by 2993
Abstract
The accurate prediction of the performance output of photovoltaic (PV) installations is becoming ever more prominent. Its success can provide a considerable economic benefit, which can be adopted in maintenance, installation, and when calculating levelized cost. However, modelling the long-term performance output of [...] Read more.
The accurate prediction of the performance output of photovoltaic (PV) installations is becoming ever more prominent. Its success can provide a considerable economic benefit, which can be adopted in maintenance, installation, and when calculating levelized cost. However, modelling the long-term performance output of PV modules is quite complex, particularly because multiple factors are involved. This article investigates the available literature relevant to the modelling of PV module performance drop and failure. A particular focus is placed on cracks and hotspots, as these are deemed to be the most influential. Thus, the key aspects affecting the accuracy of performance simulations were identified and the perceived relevant gaps in the literature were outlined. One of the findings demonstrates that microcrack position, orientation, and the severity of a microcrack determines its impact on the PV cell’s performance. Therefore, this aspect needs to be categorized and considered accordingly, for achieving accurate predictions. Additionally, it has been identified that physical modelling of microcracks is currently a considerable challenge that can provide beneficial results if executed appropriately. As a result, suggestions have been made towards achieving this, through the use of methods and software such as XFEM and Griddler. Full article
(This article belongs to the Special Issue State-of-the-Art Artificial Intelligence Models for PV Fault Detection)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop