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A self-adaptive inertia weight chaotic particle swarm algorithm

A chaotic particle swarm, inertial weight technology, applied in computing, data processing applications, special data processing applications, etc., can solve the problems of stable system operation, complex algorithms, and many parameters to adjust, and achieves guaranteed diversity and search accuracy. High, reducing the effect of fluctuations

Pending Publication Date: 2019-04-30
TIANJIN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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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

Method used

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  • A self-adaptive inertia weight chaotic particle swarm algorithm
  • A self-adaptive inertia weight chaotic particle swarm algorithm
  • A self-adaptive inertia weight chaotic particle swarm algorithm

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Embodiment Construction

[0018] An adaptive inertia weight chaotic particle swarm algorithm of the present invention is described below in conjunction with the accompanying drawings.

[0019] Please refer to figure 1 , an adaptive inertia weight chaotic particle swarm algorithm, including the following steps:

[0020] S1: Initialize the inertia weight ω 0 , acceleration factor c 1 、c 2 , population size N, maximum number of iterations N m , determine the search space [-x max , x max ] and the maximum velocity v max ;

[0021] In step S1, the velocity and position update equations of particle i are as follows:

[0022] v id (t+1)=ω·v id (t)+c 1 r 1 ·(p id (t)-x id (t))+c 2 r 2 ·(p gd (t)-x id (t)) (1)

[0023] x id (t+1)=x id (t)+v id (t+1) (2)

[0024] Among them, t is the number of particle update iterations. In generation t, the "best" position experienced by particle i in the d-dimensional space is recorded as The "best" particle position in the particle swarm is denoted a...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06Q10/04
CPCG06F30/20G06Q10/04
Inventor 游国栋苏虹霖徐涛沈延新王军李丹严宇李继生
Owner TIANJIN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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