Photovoltaic Multi-Peak MPPT Method with Improved Particle Swarm Algorithm

An MPPT control strategy based on the combination of adaptive nonlinear mutation particle swarm optimization algorithm is proposed. The algorithm uses the tanh function to control the inertia weight, balances the ability of particles to optimize globally and locally, uses the ISRU function to control the learning factor, balances the weight relationship between individual learning and social learning, and improves the convergence speed of the algorithm. The nonlinear Cauchy mutation operation is proposed to expand the scope of optimization; Finally, the perturbation and observation method is used to track locally near the maximum power point to reduce power oscillation. The simulation validates that the photovoltaic MPPT control strategy based on the improved particle swarm algorithm can effectively get the maximum power point under shading conditions and improve the output efficiency of the PV array.