The power-voltage output characteristic curves of PV series modules have multiple peaks when they are blocked by different shadows. The traditional maximum power point tracking algorithm has slow convergence speed and complex control parameters
so it can not achieve the global maximum power point tracking well. In this paper
a simplified sand cat swarm optimization algorithm is proposed
which is compared with particle swarm optimization algorithm and Gray Wolf algorithm respectively from static to dynamic under the complex lighting environment of shadow. Simulation and experiment verify that the simplified sand cat swarm optimization algorithm can realize the global photovoltaic maximum power point tracking. Under the same external conditions
compared with the other two algorithms
the simplified sand cat swarm optimization algorithm has fewer control variables affecting the experimental results. The tracking speed is increased by more than 60% and the tracking efficiency is increased by more than 0.5%. In addition
the power and voltage fluctuation in the convergence process is small
which improves the reliability of intelligent group algorithm applied in photovoltaic maximum power point tracking and improves the utilization rate of photovoltaic power generation.
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references
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