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Electronics
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AI Applications for Smart Grid
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AI Applications for Smart Grid

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 516

Special Issue Editors

Dr. Bochao Zhao

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: data analysis for demand-side resources; virtual power plant
Dr. Bo Liu

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: smart sensing for smart grids; advanced data analytics for smart grids
Dr. Ying Han

E-Mail Website
Guest Editor
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
Interests: integrated energy system; transportation electrification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) technologies are revolutionizing smart grids.  They are being extensively explored for their potential to transform the way that we generate, distribute, and consume electricity. This Special Issue will bring together cutting-edge research and innovative applications of AI for the smart grid. It is expected to highlight the significant potential of data analysis, pattern recognition, predictive modelling, etc. Meanwhile, various powerful AI applications in smart grids raise concerns and challenges, such as interpretability, collapse, data dependency, etc. This Special Issue will explore the latest advancements in AI applications for smart grids. We are particularly interested in submissions that focus on the following areas:

  • AI applications in smart sensing;
  • AI applications in fault location and protection;
  • AI applications in power markets and trading;
  • AI applications in demand-side management;
  • AI applications in integrated energy systems;
  • AI applications in transportation electrification;
  • AI applications in secure energy storage systems.

We also encourage submissions in other, related areas.

Dr. Bochao Zhao
Dr. Bo Liu
Dr. Ying Han
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. Electronics 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 2400 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
  • smart grid
  • smart sensing
  • fault location and protection
  • power market
  • demand-side management
  • integrated energy system
  • transportation electrification

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Published Papers (1 paper)

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Research

25 pages, 4826 KiB  
Article
Optimizing Photovoltaic System Diagnostics: Integrating Machine Learning and DBFLA for Advanced Fault Detection and Classification
by Omar Alqaraghuli and Abdullahi Ibrahim
Electronics 2025, 14(8), 1495; https://doi.org/10.3390/electronics14081495 - 8 Apr 2025
Viewed by 236
Abstract
The rapid growth in photovoltaic (PV) power plant installations has rendered traditional inspection methods inefficient, necessitating advanced approaches for fault detection and classification. This study introduces a novel hybrid metaheuristic method, the Dung Beetle Optimization Algorithm combined with Fick’s Law of Diffusion Algorithm [...] Read more.
The rapid growth in photovoltaic (PV) power plant installations has rendered traditional inspection methods inefficient, necessitating advanced approaches for fault detection and classification. This study introduces a novel hybrid metaheuristic method, the Dung Beetle Optimization Algorithm combined with Fick’s Law of Diffusion Algorithm (DBFLA), to address these challenges. The DBFLA enhances the performance of machine learning models, including artificial neural networks (ANNs), support vector machines (SVMs), and ensemble methods, by fine-tuning their parameters to improve fault detection rates. It effectively identifies critical faults such as module mismatches, open circuits, and short circuits. The research demonstrates that DBFLA significantly improves the performance of conventional machine learning techniques by forming a stacking classifier, achieving an individual meta-learner accuracy of approximately 98.75% on real PV datasets. This approach not only accommodates new operating modes and an expanded range of fault conditions but also enhances the reliability of fault detection schemes. The primary contribution of DBFLA lies in its ability to balance exploration and exploitation efficiently, resulting in superior classification accuracy compared to existing optimization techniques. By combining real and simulated datasets, the proposed hybrid method showcases its potential to substantially improve the precision and speed of PV fault detection models. Future work will focus on integrating these advanced models into real-time PV monitoring systems, aiming to reduce detection times and further enhance the reliability and operational efficiency of PV systems. Full article
(This article belongs to the Special Issue AI Applications for Smart Grid)
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