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Terawatt-Scale Grid-Connected Photovoltaic Systems
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Terawatt-Scale Grid-Connected Photovoltaic Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A2: Solar Energy and Photovoltaic Systems".

Deadline for manuscript submissions: 5 August 2025 | Viewed by 1819

Special Issue Editors

Prof. Dr. Mohammadreza Aghaei

E-Mail Website
Guest Editor
1. Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009 Alesund, Norway
2. Solar Energy Engineering Program, Department of Sustainable Systems Engineering (INATECH), Albert Ludwigs University of Freiburg, 79110 Freiburg, Germany
Interests: energy transition; renewable energy; photovoltaics; smart grid; enabling technologies; artificial intelligence (AI); unmanned aerial vehicle (UAV)
Special Issues, Collections and Topics in MDPI journals
Dr. Aref Eskandari

E-Mail Website
Guest Editor
Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
Interests: renewable energy; smart grid; artificial intelligence (AI); big data analytics; condition monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Photovoltaic (PV) systems are rapidly becoming widespread sources of energy supply. Cumulative PV installations surpassed 1.18 TW by the end of 2022 (and are estimated to exceed 1.5 TW, with an addition of 350.6 GW, by the end of 2023). Rapid progress was largely driven by improvements in solar cell and module efficiencies, reductions in manufacturing costs, and the capacity to generate green electricity.

Moreover, solar photovoltaic (PV) systems have become one of the fastest growing sources of renewable energy that can be integrated into the grid distribution network. However, there are certain challenges in the integration of PV systems directly with a grid.

This Special Issue is devoted to the collection of state-of-the-art ideas in GCPV power generation system. Topics of interest for this issue include, but are not limited to, the following areas:

  • Condition monitoring in GCPV;
  • Reviews;
  • Life cycle analyses;
  • Performance monitoring and case studies;
  • Trends and developments in GCPV technologies;
  • Economic analyses;
  • Energy policies related to GCPV systems;
  • Energy management storage;
  • Grid interaction;
  • New converter topologies;
  • Modulation and control techniques;
  • PV power plants with energy storage;
  • Optimization and MPPT techniques.

We look forward to receiving your contributions.

Prof. Dr. Mohammadreza Aghaei
Dr. Aref Eskandari
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 systems
  • photovoltaic
  • power control
  • grid integration
  • reliability
  • stability
  • converters (DC/DC or DC/AC)
  • MPPT
  • monitoring
  • energy policies
  • energy storage

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Published Papers (1 paper)

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Research

23 pages, 4630 KiB  
Article
Ensemble LVQ Model for Photovoltaic Line-to-Line Fault Diagnosis Using K-Means Clustering and AdaGrad
by Peyman Ghaedi, Aref Eskandari, Amir Nedaei, Morteza Habibi, Parviz Parvin and Mohammadreza Aghaei
Energies 2024, 17(21), 5269; https://doi.org/10.3390/en17215269 - 23 Oct 2024
Cited by 2 | Viewed by 1002
Abstract
Line-to-line (LL) faults are one of the most frequent short-circuit conditions in photovoltaic (PV) arrays which are conventionally detected and cleared by overcurrent protection devices (OCPDs). However, OCPDs are shown to face challenges when detecting LL faults under critical detection conditions, i.e., low [...] Read more.
Line-to-line (LL) faults are one of the most frequent short-circuit conditions in photovoltaic (PV) arrays which are conventionally detected and cleared by overcurrent protection devices (OCPDs). However, OCPDs are shown to face challenges when detecting LL faults under critical detection conditions, i.e., low mismatch levels and/or high fault impedance values. This occurs due to insufficient fault current, thus leaving the LL faults undetected and leading to power losses and even catastrophic fire hazards. To compensate for OCPD deficiencies, recent studies have proposed modern artificial intelligence (AI)-based methods. However, various limitations can still be witnessed even in AI-based methods, such as (i) most of the models requiring a massive training dataset, (ii) critical fault detection conditions not being taken into consideration, (iii) models not being accurate enough when dealing with critical conditions, etc. To this end, the present paper proposes a learning vector quantization (LVQ)-based ensemble learning model in which three LVQs are individually trained to detect and classify LL faults in PV arrays. The initial LVQ vectors are determined using the k-means clustering method, and the learning rate is optimized by the adaptive gradient (AdaGrad) optimizer. The training and testing datasets are collected according to the PV array’s current–voltage (I–V) characteristic curve, and several features are extracted based on the Canberra and chi-squared distance techniques. The model utilizes a small training dataset, considers various critical detection conditions for LL faults—such as different mismatch levels and fault impedance values—and the final experimental results show that the model achieves an impressive average accuracy of 99.26%. Full article
(This article belongs to the Special Issue Terawatt-Scale Grid-Connected Photovoltaic Systems)
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