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Paper Abstract and Keywords
Presentation 2026-03-02 10:30
Supervised Machine Learning Techniques for Fault Detection and Classification in Photovoltaic (PV) Systems
Ahmed Malouq, Shogo Muramatsu (Niigata Univ.) EA2025-80 SIP2025-100 SP2025-33
Abstract (in Japanese) (See Japanese page) 
(in English) Solar photovoltaic systems are considered one of fastest rising renewable energy technologies due to their efficiency, low cost, long-life cycle, installation flexibility and recyclability. However, these systems can be exposed to different abnormalities, like various types of faults that can affect performance and lead to significant consequences. Therefore, this paper is dedicated to detect and classify different types of faults in PV systems using Machine Learning (ML) algorithms such as, multiclass Support Vector Machine (MSVM), K-Nearest Neighbor (KNN), and Decion Tree (DT) algorithms. The aim of this study is to use the ML algorithms to classify normal operations, four types of faults using data collected from a PV system built in a real-time environment. Specific parameters are collected from the system for various values of irradiance and temperature to ensure a wide variety of environmental weather conditions are considered. The performance was improved by deploying a unique approach to choose the best input parameters for each ML algorithm that gives the highest accuracy. The results showed that the ML algorithms after parameter selection excelled in detecting and classifying normal operating conditions and different faults in PV system. MSVM achieved 96.14% overall accuracy, KNN achieved 88.1%, and DT achieved 89.7%.
Keyword (in Japanese) (See Japanese page) 
(in English) PhotoVoltaic System / Machine Learning / Support Vector Machine (SVM) / K Nearest Neighbor (KNN) / Decision Tree (DT) / Fault Detection / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 370, SIP2025-100, pp. 43-48, March 2026.
Paper # SIP2025-100 
Date of Issue 2026-02-23 (EA, SIP, SP) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF EA2025-80 SIP2025-100 SP2025-33

Conference Information
Committee SP EA SIP IPSJ-SLP  
Conference Date 2026-03-02 - 2026-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIP 
Conference Code 2026-03-SP-EA-SIP-SLP 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Supervised Machine Learning Techniques for Fault Detection and Classification in Photovoltaic (PV) Systems 
Sub Title (in English)  
Keyword(1) PhotoVoltaic System  
Keyword(2) Machine Learning  
Keyword(3) Support Vector Machine (SVM)  
Keyword(4) K Nearest Neighbor (KNN)  
Keyword(5) Decision Tree (DT)  
Keyword(6) Fault Detection  
Keyword(7)  
Keyword(8)  
1st Author's Name Ahmed Malouq  
1st Author's Affiliation Niigata University (Niigata Univ.)
2nd Author's Name Shogo Muramatsu  
2nd Author's Affiliation Niigata University (Niigata Univ.)
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Speaker Author-1 
Date Time 2026-03-02 10:30:00 
Presentation Time 20 minutes 
Registration for SIP 
Paper # EA2025-80, SIP2025-100, SP2025-33 
Volume (vol) vol.125 
Number (no) no.369(EA), no.370(SIP), no.371(SP) 
Page pp.43-48 
#Pages 6 
Date of Issue 2026-02-23 (EA, SIP, SP) 

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