ML-based Predictive Maintenance Fault Detection using Optimal Merge Pattern in Solar PV Systems

As more solar photovoltaic (PV) systems are put in place, we need better ways to find faults to make sure the systems work as well as possible and provide as much energy as possible. This paper shows how to use Optimal Merge Pattern (OMP) in a machine learning (ML)-based predictive maintenance framework to find faults in solar PV systems early and accurately. In changing weather and grid circumstances, traditional techniques of finding faults typically take too long to respond and do not work as well. The suggested system uses sensor data from PV modules and inverters, such as current, voltage, irradiance, and temperature. Optimal Merge Pattern is used to preprocess data by compressing and organising temporal input data streams to cut down on duplication and make learning faster. We use the combined data to train Random Forest and Support Vector Machine (SVM) classifiers to find frequent problems including shading, soiling, hotspot creation, and inverter failure. When compared to baseline approaches that do not use OMP, the system gets $\mathbf{9 6. 8 \%}$ of the classifications right and $\mathbf{2 1 \%}$ fewer false positives. Real-time data from a rooftop solar system shows that the OMP-based method makes predictive maintenance tactics far more responsive. The model is easy to scale up, lightweight, and works well with smart PV monitoring devices that are placed on the edge. This research helps develop smart, automated maintenance solutions that keep systems running longer and cost less to run. In the future, we will concentrate on connecting the model to IoTbased warning systems and adding more types of faults using deep learning to look at PV panels. The suggested paradigm fits with current work on AI-based solutions for sustainable energy.