Photovoltaic multi-peak power point tracking method based on territory particle swarm

Abstract

The invention relates to a photovoltaic multi-peak power point tracking method based on a territory particle swarm. The method comprises the following steps: 1) adjusting a duty ratio of a PWM as 0 toobtain output voltage of a Boost circuit; 2) setting the number of the particle swarm; 3) computing an adaptive value of each particle, re-judging and selecting a global optimal particle and the global optimal particle territory according to the obtained adaptive values; 4) determining a maximum value estimation range in the particle territory; 5) judging whether the condition that the maximum value estimation range contains the adaptive value of the global optimal particle is tenable; 6) judging whether the condition that the maximum distance dmax between the particles is less than 0.02Uocaris tenable; 7) computing each particle adaptive value, re-judging and selecting the global optimal particle; 8) judging whether the condition that the maximum distance dmax between the particles is less than 0.01Uocar is tenable; and 9) maintaining the output voltage as the voltage of the obtained global optimal particle location, and judging whether the environment changes suddenly.

Description

technical field [0001] The invention relates to the field of photovoltaic power generation, in particular to a photovoltaic multi-peak maximum power point tracking method based on territorial particle swarms. Background technique [0002] In the entire photovoltaic power generation system, photovoltaic cell technology and photovoltaic conversion control technology are two supporting technologies. Maximum power point tracking is one of the key technologies for high-efficiency photovoltaic power generation systems. At present, many maximum power tracking algorithms have been proposed for the nonlinear characteristics of the P-U characteristic curve of photovoltaic arrays. [0003] Traditional algorithms mainly include perturbation and observation method, conductance increment method, short-circuit current proportional coefficient method, sliding mode extremum search, etc. These algorithms are mainly aimed at maximum power point tracking under unshielded and uniform illuminati...

AI Technical Summary

Problems solved by technology

Traditional algorithms do not have the ability to track globally, and will fall into local extremum in the case of multiple peaks, resulting in a large amount of energy loss

Intelligent algorithms mainly include particle swarm algorithm, cuckoo algorithm, firefly algorithm, ant colony algorithm, bee colony algorithm and wolf swarm algorithm, etc. These algorithms can track the global maximum power point, but still have the disadvantage of long tracking time

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