Adaptive MPPT Control of Solar PV System using Incremental Conductance Algorithm

Solar photovoltaic (PV) systems have emerged as one of the most promising renewable energy sources due to their clean and sustainable nature. However, the output power of a PV system is highly dependent on environmental conditions such as solar irradiance and temperature. To ensure maximum energy extraction under varying operating conditions, an efficient Maximum Power Point Tracking (MPPT) technique is required. Conventional MPPT methods, including Perturb and Observe (P&O) and Incremental Conductance (INC), are widely used in PV applications. Although the P&O method offers simple implementation and fast response, it suffers from steady-state oscillations around the Maximum Power Point (MPP). On the other hand, the Incremental Conductance technique provides better accuracy but requires higher computational effort and may exhibit slower response under certain conditions. This paper proposes an Adaptive Hybrid MPPT Control Algorithm that combines the advantages of both P&O and Incremental Conductance methods. The proposed controller utilizes the fast tracking capability of the P&O algorithm during transient conditions and switches to the Incremental Conductance technique near the MPP to improve tracking accuracy and minimize power oscillations. The performance of the proposed system is evaluated using MATLAB/Simulink under different irradiance conditions. Simulation results demonstrate that the hybrid MPPT controller achieves faster convergence, improved stability, reduced oscillations, and higher tracking efficiency compared to conventional MPPT techniques. The proposed approach attains a tracking efficiency of approximately 98.8%, making it an effective solution for enhancing the performance and energy yield of solar PV systems operating under dynamic environmental conditions.