An improved quantum particle swarm photovoltaic multi‐peak mPPT method combined with Lévy flight

Abstract

AbstractInertial weight adaptive quantum particle swarm optimization (DCWQPSO) algorithm can effectively improve the problem of particle falling into local extreme value. But the particle is still possible to fall into local extreme value in the later stage of particle evolution. When it is applied to photovoltaic multi‐peak maximum power tracking (MPPT), the tracking efficiency is not only reduced, but also may lead to tracking failure under the condition of sudden tracking of photovoltaic light intensity. To solve the above problems, this paper proposes a photovoltaic maximum power tracking (MPPT) control algorithm combining Lévy flight strategy with DCWQPSO algorithm. Lévy flight is a non‐Gaussian random process. The algorithm introduces Lévy flight strategy to change the mutation formula of particles and uses the characteristics of Lévy flight short step and occasionally long step jump search to improve the diversity of particles in the algorithm population. The algorithm proposed in this paper enhances the particle diversity, improves the convergence accuracy and speed of the algorithm, and overcomes the defects of the DCWQPSO algorithm. Simulation results demonstrate that the MPPT control algorithm proposed in this paper has fast‐tracking speed and high precision, which can effectively improve the maximum power tracking efficiency and dynamic quality of photovoltaic power generation system under uncertain environment, and it also has good robustness.