Fault Detection in Solar Power Plants Based on Energy Production Data

Engineering notes

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Chinese explanation / 中文解读

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Original abstract

This research addresses the critical need for the timely identification of faults in solar power plants to minimize electricity loss. The study analyses energy production data from a Lithuanian solar power plant, comprising 143 strings distributed across 12 inverters, over a 19-month period. During data preprocessing, 16 key features were extracted from each string's time series data to represent the global structure of the data. The extraction process resulted in a transformed dataset, where each time series is represented as an object with 16 features, enabling for more effective analysis. Statistical and machine learning techniques — including PCA + $\alpha$-HULL, Isolation Forest (iForest), and Local Outlier Factor (LOF) — were employed to identify systems exhibiting abnormal behavior. The results demonstrate that a combination of these methods can help effectively identify outliers, with a combined anomaly score providing a comprehensive assessment of string performance. Additionally, RANSAC and DBSCAN methods were used to construct fault profiles, which enabled a more in-depth analysis of each system's performance and provided further confirmation of previously identified systems exhibiting abnormal behavior.

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