An improved particle swarm MPPT algorithm based on dynamic inertia weights and multi-threshold restart conditions

Abstract

The invention provides an improved particle swarm MPPT algorithm based on dynamic inertia weights and multi-threshold restart conditions and relates to the technical field of photovoltaic power generation. The algorithm dynamically resolves inertia weight values with the distance between particles and the optimal solution and the number of times of iteration as variables, and, compared with the methods with fixed inertia weights or segmented values of weights, the algorithm has the advantages of high convergence speed and small oscillation amplitude and achieves the objective of dynamically adjusting an iteration process according to search conditions. Multi-threshold restart conditions are set for complicated and ever-changing external environment, so that the algorithm, compared with theconventional fixed-cycle scanning method, prevents unnecessary voltage full-range scanning and solves the problems of frequent oscillation of systems, increase of power loss and tracking lag resulting from fixed cycles of the fixed-cycle scanning method; compared with the method with a single condition of using power change for judgment, the algorithm enables the change of the external environment to be identified more accurately and solves the problem that a system cannot be restarted normally because of monitoring failure caused by the single judgment condition.

Description

technical field [0001] The invention relates to the technical field of photovoltaic power generation, in particular to the technical field of photovoltaic maximum power tracking. Background technique [0002] At present, a variety of MPPT algorithms have been proposed at home and abroad, including the perturbation and observation method and the conductance incremental method. However, these algorithms are only suitable for the case where the output of the photovoltaic array exhibits a single-peak characteristic under uniform lighting conditions. Due to the fact that in practical application, the photovoltaic array will inevitably be blocked by local shadows caused by cloud movement and dirt, which will cause differences in the working conditions of the photovoltaic modules in the array, and the output characteristic curve of the photovoltaic array will change from a single peak to a multi-peak. The conventional MPPT algorithm is easy to search for local extreme points by mi...

AI Technical Summary

Problems solved by technology

The conventional MPPT algorithm is easy to search for local extreme points by mistake, which reduces the power generation of photovoltaic arrays and causes energy loss

In order to solve the above problems, scholars at home and abroad have proposed a variety of improved MPPT algorithms. For example, the article [1] proposed that the open-circuit voltage of photovoltaic modules based on the voltage difference of adjacent peak points is about 0.8 times, and the conductance incremental method is used to search for partitions. , this method has some defects: the selection of the tracking range depends on experience, and it can only be tracked in a known single environment, and changes in the external environment will lead to search errors

The disadvantage of this method is that the scanning frequency is high, and the voltage changes from zero to the maximum value, and the scanning range is large, which will cause frequent oscillation of the system and increase power loss.

And because of the periodic scanning, when the shadow changes, the system cannot respond in time

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