AI-Based Fault Diagnosis for Photovoltaic Systems Using Performance Indicators Derived from Low-Resolution Instantaneous Data

sukwhan Ko; woogyun Shin; youngchul Ju; hyemi Hwang; jinsuk Lee; byungki Jeon

doi:10.7836/kses.2026.46.5.001

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2026 Vol.46, Issue 5 Next Page

AI-Based Fault Diagnosis for Photovoltaic Systems Using Performance Indicators Derived from Low-Resolution Instantaneous Data 저해상도 순시 데이터 기반 태양광 성능지표를 활용한 인공지능 기반 이상진단 모델 개발

30 October 2026. pp. 1-15

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Abstract

The performance evaluation and operation and maintenance (O&M) of photovoltaic (PV) power plants are primarily based on key performance indicators (KPIs) derived form monitoring data, including Performance Ratio (PR) and Capacity Factor (CF). In this study, we evaluated the feasibility of applying unsupervised learning-based toward anomaly detection using daily KPIs generated from conventional 10-min interval monitoring data. Daily CF and temperature-corrected PR were calculated from approximately 4.5 years of operational data collected from an 88.2 kWp PV power plant and used as input features for Isolation Forest (i-Forest), k-means, and Gaussian Mixture Model (GMM). We found that CF is suitable for long-term performance assessment but is limited in its capacity to detect anomalies, whereas PR is highly sensitive to performance degradation but is also susceptible to irradiance sensor measurement errors. Therefore, CF and PR facilitates the characterization of both degradation-related anomalies and measurement-related abnormalities. Among the evaluated models, GMM exhibited the best clustering performance by clearly identifying normal operation, performance degradation, low-irradiance operation, and irradiance sensor anomalies. These results demonstrate the feasibility of using daily KPIs derived monitoring data for the unsupervised identification of abnormal operating patterns. This study highlights the feasibility of developing practical and cost-effective AI-based O&M technologies for PV power plants using existing monitoring infrastructure.

Keywords

Key Performance Indicator

Capacity Factor

Performance Ratio

Fault Diagnosis

Artificial Intelligence

키워드

성능지표

이용률

성능계수

고장진단

인공지능

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Information

Publisher :Korean Solar Energy Society
Publisher(Ko) :한국태양에너지학회
Journal Title :Journal of the Korean Solar Energy Society
Journal Title(Ko) :한국태양에너지학회 논문집
Volume : 46
No :5
Pages :1-15
Received Date : 2026-07-10
Revised Date : 2026-08-07
Accepted Date : 2026-08-20
DOI :https://doi.org/10.7836/kses.2026.46.5.001

Journal of the Korean Solar Energy Society ISSN:1598-6411(Print) 2508-3562(Online) 한국태양에너지학회 논문집

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