Performance Improvement of Photovoltaic MPPT Using a Hybrid Strategy

Power optimization in PV systems has significantly benefited from recent advances in artificial intelligence and intelligent control techniques. This paper presents a hybrid MPPT strategy designed for PV systems operating under dynamic irradiance.

The proposed approach combines the Grey Wolf Optimizer, a nature-inspired metaheuristic algorithm, with the Super-Twisting Algorithm, a second-order sliding mode control technique. In the proposed framework, GWO is employed to optimally tune the STA parameters, enhancing the MPPT performance. Unlike conventional MPPT methods that treat optimization and control separately, the proposed STA–GWO strategy integrates both functions into a unified control framework, improving transient response while reducing steady-state oscillations.

The proposed controller was evaluated in MATLAB/Simulink using a 400 Wp PV generator composed of five Suntech STP-80 modules connected in series and interfaced with a DC–DC boost converter. The system was tested under a dynamic irradiance profile varying from 400 to 1000 W/m², including both abrupt and gradual changes, while the cell temperature was maintained at 25°C. Performance was assessed using tracking efficiency, convergence time, and Root Mean Square Error.

Simulation results demonstrate that the proposed STA–GWO controller achieves a convergence time of 2–3 ms, residual power oscillations below 0.1%, and an average tracking efficiency of 99.38% under dynamic irradiance conditions. Compared with conventional MPPT techniques such as Perturb and Observe (P&O) and Incremental Conductance (INC), the proposed strategy provides additional energy gains of approximately 3% under medium irradiance, 5% under high irradiance, and up to 10% during rapid irradiance fluctuations.

These results confirm that the proposed approach provides an effective compromise between dynamic response and steady-state accuracy. Although the present study is limited to simulation results, future work will focus on real-time implementation and experimental validation to further assess the practical feasibility and robustness of the proposed MPPT strategy.