Improving PV Plant Monitoring with a Season-Aware Clustering and Outlier Detection Method
Photovoltaic (PV) power plants have become an important source of clean electricity. To keep them running efficiently, it is essential to detect faults and unusual behavior. However, this is not easy because data from PV power plants are noisy and strongly affected by environment, seasonal changes, and weather conditions. Reliable anomaly detection could help with preventing power losses, reducing maintenance costs, and extending the lifetime of the system. This paper presents a two step approach for detecting anomalies in PV plant data. First, K-means clustering is used to group days with similar conditions based on daily energy metrics. Then, the Local Outlier Factor (LOF) algorithm is applied within each cluster to find whether any anomalies are present. For visualization and interpretability, results are presented in a graphic form. Initial application demonstrates the framework's ability to segment seasonal data and identify extreme deviations from normal operating clusters. Although larger datasets are still needed for full validation, the method provides a foundation for season-aware monitoring.