Multi-peak MPPT Optimization Control Based on Natural Selective Mechanism and Genetic Algorithm
When partial shading occurs,the P-U output curve of the photovoltaic array will exhibit multiple peaks.To solve the problems of slow response,significant oscillations,and susceptibility to local optima in traditional particle swarm optimization algorithms for maximum power point tracking(MPPT)in photovoltaic systems,an optimization method that combines the natural selection mechanism in genetic algorithms with particle swarm optimization is proposed.The core idea of this method is to sort all particles in the population by fitness in each iteration,and replace the worst half of the population with the best half in terms of ve-locity and position.This can improve the search range and global optimization performance.In addition,the learning factor and iner-tia weight are optimized and adjusted to improve the convergence speed and accuracy of the algorithm.This method can effectively solve the problems of slow response,significant oscillations,and susceptibility to local optima in particle swarm optimization algo-rithms for photovoltaic MPPT and has better optimization performance.To achieve more efficient photovoltaic MPPT,this method can be widely applied.
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维普
