NSNPSO-INC: A Simplified Particle Swarm Optimization Algorithm for Photovoltaic MPPT Combining Natural Selection and Conductivity Incremental Approach

To enhance photovoltaic (PV) system performance under various environmental conditions, this paper proposes a hybrid Maximum Power Point Tracking (MPPT) algorithm that integrates the Incremental Conductance (INC) method with an improved Particle Swarm Optimization (PSO) algorithm. The proposed approach simplifies the PSO velocity update process and uses natural selection to filter less adaptive particles. Leveraging the rapid convergence of the INC method, this hybrid algorithm effectively tracks the maximum power point (MPP). Simulation results show that under static partial shading conditions, the NSNPSO-INC algorithm achieves a stable MPPT efficiency of 99.9%, with a 68.18% faster convergence than conventional PSO and more stable performance than traditional INC. Under dynamic irradiance conditions, NSNPSO-INC adapts quickly, improving convergence speed by 93.33% over conventional PSO. Experimental results further demonstrate that NSNPSO-INC reaches steady state fastest, with an 85.52% improvement in convergence speed over traditional PSO. The algorithm maintains a stable power output around 550W even under cloudy conditions, significantly enhancing PV energy conversion efficiency and providing valuable insights for future MPPT algorithm development.