A Novel Approach for PV Cell Fault Detection Using YOLOv8 and Particle Swarm Optimization

Quoc Bao Phan, Tuy Tan Nguyen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

This paper introduces a novel approach for detecting faults in photovoltaic (PV) cells. The proposed method combines You Only Look Once version 8 (YOLOv8) and the Particle Swarm Optimization (PSO) architecture to enhance detection accuracy. Unlike existing methods, the proposed model leverages PSO to optimize the parameters of YOLOv8. To evaluate the effectiveness of the approach, two study cases are conducted using training sets of 70% and 80%, respectively. The PV system data is utilized as input, with YOLOv8 extracting features to detect faulty cells. The PSO algorithm optimizes the model's parameters to achieve the highest detection accuracy. Experimental results demonstrate that the proposed approach outperforms existing fault detection methods in terms of accuracy and robustness, achieving a mean Average Precision at 50 (mAP@50) of 94%. By harnessing the power of YOLOv8 and PSO, the approach offers a promising solution for reliable and efficient fault detection in PV systems, making it a viable option for enhancing system performance and reducing maintenance costs.

Original languageEnglish (US)
Title of host publication2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages634-638
Number of pages5
ISBN (Electronic)9798350302103
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023 - Tempe, United States
Duration: Aug 6 2023Aug 9 2023

Publication series

NameMidwest Symposium on Circuits and Systems
ISSN (Print)1548-3746

Conference

Conference2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023
Country/TerritoryUnited States
CityTempe
Period8/6/238/9/23

Keywords

  • deep neural network
  • optimization
  • PV cell fault detection
  • YOLOv8

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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