A DATA-DRIVEN FRAMEWORK FOR INTELLIGENT OPERATIONAL MONITORING OF HYBRID PHOTOVOLTAIC POWER SYSTEMS

Abstract

The increasing deployment of hybrid photovoltaic (PV) power systems have created a growing need for intelligent operational monitoring to improve system reliability and maintenance. However, conventional monitoring techniques often fail to distinguish operational faults from weather-induced performance variations. This study presents a data-driven framework for intelligent operational monitoring by integrating residual analysis, statistical validation, and unsupervised K-means clustering. The framework was validated using three years of operational and meteorological data (June 2022–May 2025) from a 20 kWp hybrid PV system at the University of Nigeria, Nsukka. Expected daily energy output was estimated from solar irradiance and installed capacity, while residual analysis quantified deviations between expected and measured performance. The proposed framework analysed 1,488 operating conditions, identifying 640 anomalous (43.0%) and 848 normal (57.0%) conditions. The K-means model achieved a silhouette score of 0.62, indicating good cluster separation, with anomaly and normal cluster centroid residuals of −35.21 kWh and −96.47 kWh, respectively. The analysis revealed persistent negative residuals, zero-generation periods, and inverter interruptions, demonstrating that performance losses were primarily operational rather than environmental. The proposed framework distinguished weather-induced variability from technical faults and provides a practical decision-support tool for intelligent operational monitoring, early fault detection, predictive maintenance, and improved reliability of hybrid photovoltaic power systems in tropical environments.