MPPT control method based on butterfly optimization algorithm

Abstract

The invention relates to a maximum power point tracking (MPPT) control method based on a butterfly optimization algorithm, which comprises the following steps of: 1) defining a duty ratio as a butterfly individual, and defining the highest butterfly concentration as an optimal butterfly individual; selecting i butterfly individuals, selecting the number of iterations, and initializing the flight space position of the butterfly, wherein the output duty ratio is used for controlling the output power; 2) when the photovoltaic array operates stably, measuring output voltage at two ends of the load and output current flowing through the load; 3) calculating the output power of the butterfly at the moment t; 4) introducing cosine similarity to screen butterfly individuals; 5) comparing the butterfly power values, and outputting an optimal duty ratio and an optimal power value; 6) changing the step length of the butterfly algorithm; 7) judging whether all detection is finished, if so, outputting and keeping the optimal duty ratio, and otherwise, continuing; and 8) judging whether the external illumination intensity changes suddenly, if so, executing MPPT again, and otherwise, continuing to maintain the optimal duty ratio. The method has the advantages of improving the MPPT convergence speed, improving the solar energy utilization rate and the like.

Description

technical field [0001] The invention relates to the technical field of solar power generation, in particular to an MPPT control method based on a butterfly optimization algorithm. Background technique [0002] Traditional MPPT maximum power tracking methods include conductance incremental method, perturbation and observation method, constant voltage method, open circuit voltage proportional coefficient method and so on. When partial shadowing occurs in the photovoltaic array, using the traditional MPPT maximum power tracking method to track the MPP point (Maximum Power Point, maximum power point) will fall into a local optimal situation, so that the maximum power point cannot be tracked. Therefore, in order to solve such problems, some scholars have used intelligent algorithms to solve the problem of local shading, such as gray wolf optimization algorithm, improved particle swarm optimization algorithm, moths to flame algorithm and so on. [0003] However, in the case of pa...

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Problems solved by technology

[0003] However, in the case of partial shading, the traditional MPPT algorithm cannot jump out of the local optimum to find the maximum power point. That is, to solve the problem of multiple peaks in the P-V curve of the photovoltaic array system under partial shading conditions, the traditional MPPT algorithm is used Misjudgment may occur in the control, resulting in failure to track the MPP point, so that the solar energy is not fully utilized

Although the improved gray wolf algorithm can effectively track the global maximum power, its implementation algorithm is relatively complicated; although the whale algorithm has powerful global and local search capabilities, its implemented functions are relatively complex and easily fall into local optimum. Condition

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