A self-adaptive inertia weight chaotic particle swarm algorithm

Abstract

The invention relates to the field of photovoltaic power generation, in particular to an adaptive inertial weight chaotic particle swarm optimization (ACPSO) algorithm. The algorithm utilizes segmented Logistic mapping with higher efficiency than general Logistic mapping to generate a chaotic sequence to initialize the positions of particles, so that the diversity of global search is ensured; then, a particle swarm algorithm is optimized by adopting an adaptive inertia weight, so that the tracking speed of the maximum power is increased; and finally, if the judgment algorithm falls into precocity, performing extreme value disturbance on the particle optimal position and the global optimal position at the same time. Compared with single extreme value disturbance, the method can enable the algorithm to jump out of local optimum faster. According to the algorithm, the maximum power point can be tracked more quickly and effectively in real time according to sunlight changes, the system works near the maximum power point, meanwhile, the oscillation phenomenon of the system at the maximum power point is reduced, and the utilization rate of a photovoltaic array is increased.

Description

technical field [0001] The invention relates to the field of photovoltaic power generation, in particular to an adaptive inertia weight chaotic particle swarm algorithm. Background technique [0002] Due to the limitations of traditional energy sources and the increasingly prominent environmental problems, clean and renewable energy has attracted more and more attention from researchers at home and abroad. Because of its advantages of wide distribution and no pollution, solar energy will become one of the most promising renewable energy sources in the future. In practice, dust on the surface of the photovoltaic array, surrounding buildings and clouds will reduce its power generation efficiency. Therefore, it is particularly important to track and control the maximum power point of the photovoltaic system. Under partial shading conditions, the power-voltage (P-U) curve of the photovoltaic system has a multi-peak characteristic, which makes conventional maximum power tracking...

AI Technical Summary

Problems solved by technology

These control strategies have their own advantages and disadvantages. For example, sliding mode variable structure control has the advantages of fast response and strong robustness, but the system oscillates after reaching a steady state; fuzzy control requires experience to determine parameters. There are still some fluctuations in the steady state; although the genetic algorithm can track the maximum power point, it cannot make the system work stably at the maximum power point, and the algorithm is more complicated, and there are many parameters to be adjusted; the particle swarm algorithm is relatively simple, There are relatively few parameters to be adjusted, and it has a good global search ability, but how to determine the optimal parameters is a very complicated optimization problem

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