SAL Silicon Austria Labs GmbH, Europastr. 12, 9524 Villach, Austria.
Fronius International GmbH, Guenter Fronius Straße 1, 4600 Thalheim bei Wels, Austria.
Sensors (Basel). 2021 May 27;21(11):3733. 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.
由于多种因素，光伏（PV）电站的性能通常会随着时间的推移而显著下降。运行和维护系统旨在通过分析监测数据并应用数据驱动方法来评估性能下降的原因，从而提高光伏电站的效率和盈利能力。存在两种主要的退化类型，一种是光伏系统的逐渐退化，另一种是突然出现异常。这促使我们开发和实施统计方法，以经济有效的方式可靠且准确地检测性能问题。在本文中，我们为逐渐退化评估和异常检测引入了不同的方法。根据光伏电站监测系统中可用的数据，可以为每个退化类别选择适当的方法。在位于斯洛文尼亚和意大利的三个不同的光伏电站经过数年监测所获得的数据上，演示了所引入方法的性能。我们的工作得出的结论是，所引入的方法可以有助于及时准确地识别光伏电站的逐渐退化和突然异常。
