Enhancing energy efficiency with different mppt algorithms in photovoltaic systems

Renewable energy sources play a vital role in meeting the increasing global energy demand and mitigating the environmental impacts of fossil fuels. Among these sources, solar energy stands out for its ability to generate electricity directly, its environmentally friendly nature, and its sustainability. The performance of photovoltaic (PV) systems, which convert solar energy into electricity, is significantly affected by environmental factors such as irradiance, temperature, and shading. To ensure maximum efficiency under varying conditions, various Maximum Power Point Tracking (MPPT) algorithms have been developed. In this thesis, four MPPT algorithms aimed at enhancing energy efficiency in PV systems are analyzed: Particle Swarm Optimization (PSO), Artificial Neural Networks (ANN), Fuzzy Logic, and a novel hybrid algorithm developed as part of this study, the Perturb and Observe–Sliding Mode Control (P&O–SMC) hybrid method. This approach combines the low computational cost of the Perturb and Observe (P&O) method with the rapid adaptation and stability of the Sliding Mode Control (SMC) technique, resulting in a flexible, low-oscillation, and fast-response control strategy against environmental variations. All algorithms were tested in MATLAB/Simulink under a fixed irradiance of 1000 W/m² and a temperature range of 0°C to 60°C. The simulation results demonstrated that the P&O–SMC hybrid algorithm achieved the highest performance, with a 92% success rate and 230 W power output, outperforming the other methods. The PSO algorithm followed with an 85.83% success rate and 214.58 W, showing strong convergence characteristics and effective adaptation to changing conditions. The ANN and Fuzzy Logic methods yielded lower performances, with success rates of 66.67% and 63.33%, and power outputs of 166.67 W and 158.33 W, respectively. The findings confirm that the P&O–SMC hybrid method is the most effective MPPT approach among those evaluated, especially under dynamic environmental conditions. Its high efficiency, stability, and low computational burden make it a promising solution for PV systems with limited hardware resources. While PSO also provides high efficiency and fast response due to its global search capability, the P&O–SMC method offers more consistent results across all test conditions. ANN and Fuzzy Logic methods, although viable under certain scenarios, demonstrated limited effectiveness overall. This thesis offers a comparative performance evaluation of MPPT algorithms and presents the effectiveness of an originally developed hybrid control approach, contributing to the advancement of energy efficiency in photovoltaic systems.