Machine Learning Assisted Predictive Maintenance of Solar Photovoltaic Systems Using Electronic Sensor Data

This study presents an electronics-based predictive maintenance framework for solar photovoltaic systems using sensor monitoring and machine learning.  Continuous acquisition of electrical and environmental parameters enables accurate health assessment of PV modules and early fault detection. The proposed system can improve energy generation efficiency, reduce maintenance costs, and increase system reliability. The integration of embedded electronics, IoT communication, and predictive analytics represents an effective solution for modern renewable energy applications.

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