Statistical Methods for Degradation Estimation and Anomaly Detection in Photovoltaic Plants

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 11; p. 3733
• Main Authors: Dimitrievska, Vesna, Pittino, Federico, Muehleisen, Wolfgang, Diewald, Nicole, Hilweg, Markus, Montvay, Andràs, Hirschl, Christina
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 27.05.2021; MDPI
• Subjects: Anomalies; Decomposition; degradation analysis; Failure; failure detection; Machine learning; Monitoring systems; operation & maintenance; Performance degradation; Plant monitoring; PV system; Regression analysis; Statistical methods; Thermal cycling
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21113733

Photovoltaic (PV) plants typically suffer from a significant degradation in performance over time due to multiple factors. Operation and maintenance systems aim at increasing the efficiency and profitability of PV plants by analyzing the monitoring data and by applying data-driven methods for assessing the causes of such performance degradation. Two main classes of degradation exist, being it either gradual or a sudden anomaly in the PV system. This has motivated our work to develop and implement statistical methods that can reliably and accurately detect the performance issues in a cost-effective manner. In this paper, we introduce different approaches for both gradual degradation assessment and anomaly detection. Depending on the data available in the PV plant monitoring system, the appropriate method for each degradation class can be selected. The performance of the introduced methods is demonstrated on data from three different PV plants located in Slovenia and Italy monitored for several years. Our work has led us to conclude that the introduced approaches can contribute to the prompt and accurate identification of both gradual degradation and sudden anomalies in PV plants.