MPPT control method based on molecular motion trail search algorithm

Abstract

The invention discloses a multi-peak maximum power point tracking (Maximum Power Point Tracking, MPPT) control method based on a molecular motion trail search algorithm. Firstly, short-circuit currentIsc of a photovoltaic array is fused into H population particles, and P-Isc serves as a search range of the population particles. The motion trail of the maximum power point tracked by population particles in the region is adjusted to be oval, and the global maximum power point of the photovoltaic array with high efficiency and high coverage are searched. The precision of the power point separated from the local extreme value is improved through an improved disturbance observation method. According to the algorithm, the MPPT maximum power point of the photovoltaic array with the multi-peak output characteristic under the local shadow can be searched, the problems that the MPPT maximum power point is prone to falling into a local extreme value, the precision is too low and the like are solved, and the global maximum power point is accurately found. The method is high in optimization capability, high in search precision and easy to program and implement.

Description

technical field [0001] The invention relates to a partial shadow photovoltaic array MPPT method, in particular to a multi-peak MPPT control based on a molecular trajectory search algorithm. Background technique [0002] Photovoltaic power generation is a new type of renewable source technology. In the process of photovoltaic power generation, improving the efficiency of photovoltaic power generation has always been a research hotspot. Maximum power point tracking (MPPT) is one of the main measures to improve the efficiency of photovoltaic power generation. For photovoltaic modules under consistent lighting conditions, the output P-Isc of the photovoltaic array is a single-peak curve. In order to solve the unimodal MPPT tracking problem, the perturbation observation method, conductance increment method, and constant voltage method were successively proposed, all of which have fast convergence speed in unimodal and have good tracking ability. However, for photovoltaic arrays...

AI Technical Summary

Problems solved by technology

However, for photovoltaic arrays with multiple peaks under non-uniform illumination conditions, it is difficult to find the maximum power point with the conventional MPPT tracking method based on single-peak optimization, and it is often easy to fall into local extremum in multi-peak optimization, reducing energy conversion efficiency

[0003] Conventional algorithms are difficult to achieve photovoltaic multi-peak optimization

Some composite control methods based on the combination of particle swarm optimization algorithm and perturbation-and-observation method, these methods are complicated to program and have many procedures, once a problem occurs, it is difficult to maintain, some experts also propose to use the law between the local maximum power point voltage and the open circuit voltage to Determine the search range of the incremental conductance method, but this is difficult to apply to changes in the dynamic environment, and it is easy to misjudge. Some scholars in related fields have introduced the Metropolis selection mechanism of the simulated annealing algorithm on the basis of the particle swarm optimization algorithm to search for the maximum power point. is excellent, but this method uses nested iterative double judgment conditions, which will slow down the convergence speed of the algorithm and have a certain impact on the search of the area where the optimal value is located.

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