Photovoltaic maximum power tracking method based on improved glowworm algorithm

Abstract

The invention relates to a photovoltaic maximum power tracking method based on an improved glowworm algorithm. The method comprises the following steps: step S1: loading an initial vaccine base; stepS2: setting population size, initializing positions of all glowworms in the population according to the vaccine base and taking objective function values of all the glowworms as respective maximum fluorescence brightness; step S3: dividing perception radii of all the glowworms and updating attraction degrees; step S4: updating positions of all the glowworms; step S5: performing immunization supplementing operation; step S6: determining whether a convergence condition is met or not, if so, performing the step S7, if not, returning the step S4; step S7: acquiring maximum power positions according to current positions of all the glowworms and updating the vaccine base. Compared with the prior art, due to addition of the vaccine base and the immunization supplementing operation, failure of thealgorithm is effectively avoided. Convergence speed is increased while control effect is achieved. Rapid response is achieved in a dynamic environment. Additionally, a step size function is created so that steady state oscillations of the algorithm are reduced.

Description

technical field [0001] The invention relates to a photovoltaic power control technology, in particular to a photovoltaic maximum power tracking method based on an improved firefly algorithm. Background technique [0002] Ideally, the photovoltaic cells in the photovoltaic array work under the same temperature and solar irradiance. At this time, the P-U curve of the photovoltaic array presents a single-peak characteristic. The maximum power point can be achieved by using the incremental method and the disturbance observation method. track. However, in actual situations, aging, local shading, and dust coverage will cause inconsistent output characteristics of photovoltaic cells. At this time, there are multiple power peak points on the P-U curve of the photovoltaic array, and the traditional single-peak MPPT method may be trapped in local The peak point not only causes a large amount of energy loss, but also increases the complexity of photovoltaic array scheduling. [0003]...

AI Technical Summary

Problems solved by technology

However, most swarm intelligence algorithms have disadvantages such as: restarting is required when external conditions change; under dynamic PSC, the algorithm does not converge or the convergence time is long; the convergence time is unstable, etc.

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