Anomaly Detection of Photovoltaic Systems Installed in Renewable Energy Housing Support Project Sites by Analyzing Power Generation Data

Dawon Kim; Sung-Min Kim; Jangwon Suh; Yosoon Choi

doi:10.7836/kses.2022.42.1.033

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2022 Vol.42, Issue 1 Preview Page Next Page

Research Article

Anomaly Detection of Photovoltaic Systems Installed in Renewable Energy Housing Support Project Sites by Analyzing Power Generation Data 발전량 데이터 분석을 통한 신재생에너지 주택지원사업지의 태양광발전 시스템 이상 감지

28 February 2022. pp. 33-46

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Abstract

In this study, we proposed a new method of detecting abnormalities by analyzing power generation data of photovoltaic (PV) systems installed in renewable energy housing support project sites. The study site is north of Gakbuk-myeon, Cheongdo-gun, Gyeongsangbuk-do, Korea, where 63 PV systems have been installed and operated. Based on the system design and surrounding environment, the 63 PV systems were clustered into 6 groups using the K-means clustering method, which is an unsupervised machine learning algorithm. The power production data from the PV systems in each group were analyzed and set as abnormal values if they deviated from the range of ±2.58 times the standard deviation from the mean (assuming a normal distribution and 99% confidence interval). As a result, several abnormalities were detected in the PV systems in November 2020. The cause of the abnormalities was confirmed through site investigation. The proposed method is expected to accelerate the diagnosis of PV systems in renewable energy housing support project sites.

Keywords

Renewable energy

Photovoltaic system

Monitoring system

Machine learning

Unsupervised learning

키워드

재생에너지

태양광발전시스템

모니터링시스템

머신러닝

비지도학습

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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 : 42
No :1
Pages :33-46
Received Date : 2021-10-30
Accepted Date : 2021-11-09
DOI :https://doi.org/10.7836/kses.2022.42.1.033

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

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