Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems

• Published in: Energies (Basel) Vol. 13; no. 3; p. 737
• Main Authors: Park, Sunme, Park, Soyeong, Kim, Myungsun, Hwang, Euiseok
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-02-2020
• Subjects: Alternative energy sources; Artificial intelligence; Back propagation; Clustering; clustering-based pv fault detection; Fault detection; Fault diagnosis; Irradiance; Meteorological data; Neural networks; Photovoltaics; pv fleet; Roofs; self-imputation; Simulation; Solar energy; Support vector machines; unsupervised learning; Weather
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13030737

This work proposes a fault detection and imputation scheme for a fleet of small-scale photovoltaic (PV) systems, where the captured data includes unlabeled faults. On-site meteorological information, such as solar irradiance, is helpful for monitoring PV systems. However, collecting this type of weather data at every station is not feasible for a fleet owing to the limitation of installation costs. In this study, to monitor a PV fleet efficiently, neighboring PV generation profiles were utilized for fault detection and imputation, as well as solar irradiance. For fault detection from unlabeled raw PV data, K-means clustering was employed to detect abnormal patterns based on customized input features, which were extracted from the fleet PVs and weather data. When a profile was determined to have an abnormal pattern, imputation for the corresponding data was implemented using the subset of neighboring PV data clustered as normal. For evaluation, the effectiveness of neighboring PV information was investigated using the actual rooftop PV power generation data measured at several locations in the Gwangju Institute of Science and Technology (GIST) campus. The results indicate that neighboring PV profiles improve the fault detection capability and the imputation accuracy. For fault detection, clustering-based schemes provided error rates of 0.0126 and 0.0223, respectively, with and without neighboring PV data, whereas the conventional prediction-based approach showed an error rate of 0.0753. For imputation, estimation accuracy was significantly improved by leveraging the labels of fault detection in the proposed scheme, as much as 18.32% reduction in normalized root mean square error (NRMSE) compared with the conventional scheme without fault consideration.