Abstract
Operation anomalies are common phenomena in large-scale solar farms. Effective anomaly detection and classification is essential for improving operation reliability and electricity generation. However, this is a challenging task due to the high complexity and wide variety of frequently occurring anomalies. Furthermore, existing preinstalled supervisory control and data acquisition systems (SCADA) can only provide a limited amount of information regarding the healthy condition of solar farms, making accurate anomaly detection and classification difficult. This paper presents a data-driven anomaly detection and classification solution, which can accurately detect and classify diverse photovoltaic system anomalies. The proposed solution does not require additional equipment or non-SCADA data collection. More specifically, the proposed work consists of two methods: 1) a hierarchical context-aware anomaly detection method using unsupervised learning; and 2) a multimodal anomaly classification method. The proposed solution has been deployed in two large-scale solar farms (39.36 and 21.62 MWp). Multimonth operation demonstrates the effectiveness, robustness, and cost and computation efficiency of the proposed solution.
Original language | English |
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Pages (from-to) | 1351 - 1361 |
Journal | IEEE Transactions on Sustainable Energy |
Volume | 10 |
Issue number | 3 |
Early online date | 24 Aug 2018 |
DOIs | |
Publication status | Published - Jul 2019 |
Keywords
- Anomaly detection
- anomaly classification
- photovoltaic system
- machine learning