Anomaly detection based on AI in hybrid PV systems: From unsupervised constraints to supervised learning enhancement

This article discusses enhancing performance and reliability of hybrid photovoltaic (PV) systems with enhanced anomaly detection. Various studies were carried out, but few utilized small datasets or simple models that could not reflect the depth of modern PV systems with batteries and grid interfacing. In this current research work, a practical hybrid PV system of solar generation, battery storage, and grid interfacing has been studied. The novel approach utilizes unsupervised learning algorithms— Isolation Forest and One-Class SVM—to detect inverter data anomalies automatically. Through extensive visualization and correlation analysis, the models were successful in detecting anomalies such as production inconsistencies, abnormal battery behavior, and abnormalities concerning the grid. Results demonstrate that unsupervised techniques can be a good first level of smart monitoring without relying on labeled data. Besides, the paper also points out the economic benefit of using inverter loggers in fast and cost-effective data acquisition. With the provision of a flexible platform for different hybrid configurations, this work is aimed at improved anomaly detection and predictive maintenance in renewable energy systems towards more resilient and autonomous PV monitoring tools.