Statistical Methods for Degradation Estimation and Anomaly Detection in Photovoltaic Plants

Vesna Dimitrievska, Federico Pittino, Wolfgang Muehleisen, Nicole Diewald, Markus Hilweg, Andràs Montvay, Christina Hirschl

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number11
Pages (from-to)3733
Number of pages1
Issue number11
Publication statusPublished - 1 Jun 2021


  • Degradation analysis
  • Failure detection
  • Machine learning
  • Operation & maintenance
  • PV system


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