TY - M-10160
AU - Sambhus, Swapnil
TI - Advanced Non-Invasive Health Monitoring for Solar PV Panels Using an Enhanced Ensemble Classifier Approach
T2 - Scientific Research Journal of Science, Engineering and Technology

PY - 2024
VL - 2
IS - 2
SN - 2584-0584

AB - Maintaining the efficiency of solar photovoltaic (PV) systems is crucial for optimal energy production. Traditional invasive methods for diagnosing PV panel health are labor-intensive and time-consuming. This paper presents an advanced, non-invasive diagnostic approach that uses an enhanced ensemble classifier to identify faults, degradation, and performance issues in solar PV panels. By leveraging multiple machine learning models, the ensemble classifier accurately assesses the condition of panels based on non-invasive input parameters like voltage, current, and temperature. Simulation results validate the improved accuracy and robustness of the proposed method over individual classifiers.

KW - Solar PV panels
KW -  Non-invasive health monitoring
KW -  Ensemble classifier
KW -  Fault detection
KW -  Machine learning
KW -  Diagnostic accuracy
KW -  Renewable energy
KW -  Voltage
KW -  Current
KW -  Temperature analysis
DO -