Comparative Analysis of Incremental Conductance and Random Forest Methods for Maximum Power Point Tracking in Solar PV Systems

The efficiency of solar photovoltaic (PV) systems heavily relies on the optimal operation of the Maximum Power Point Tracking (MPPT) algorithm, which ensures that the system operates at its maximum power output despite varying environmental conditions. This paper presents a comparative analysis of two MPPT control methods: the Incremental Conductance (IncCond) method and Random Forest (RF) regression, implemented in MATLAB Simulink. The IncCond method, known for its precise tracking performance under varying irradiance and temperature conditions, is compared with the machine learning-based Random Forest method, which leverages historical data for adaptive learning and decision-making. Both models are simulated under real-world conditions, and their performance is evaluated using key metrics such as tracking efficiency, convergence time, and power output accuracy. The results indicate that while the IncCond method demonstrates reliable performance and faster convergence, the Random Forest-based method offers enhanced adaptability and robustness to fluctuating environmental conditions. This study highlights the potential of machine learning techniques in improving the performance of MPPT algorithms, with Random Forest emerging as a promising alternative to traditional methods.