Improved particle swarm optimization algorithm for solving power system economic dispatch problem

被引：0
|
作者：
Liang J. [1 ]
Ge S.-L. [2 ]
Qu B.-Y. [3 ]
Yu K.-J. [1 ]
机构：
[1] School of Electrical Engineering, Zhengzhou University, Zhengzhou
[2] Industrial Technology Research Institute, Zhengzhou University, Zhengzhou
[3] School of Electric & Information Engineering, Zhongyuan University of Technology, Zhengzhou
来源：
Kongzhi yu Juece/Control and Decision | 2020年 / 35卷 / 08期
关键词：
Benchmark functions; Constraint handling; Function optimization; Opposition-based learning; Particle swarm optimization algorithm; Power system economic dispatch problem;
D O I：
10.13195/j.kzyjc.2018.1490
中图分类号：
学科分类号：
摘要：
The power system economic dispatch problem is an important research topic in power systems. To solve this problem, an improved particle swarm optimization (ODPSO) algorithm is proposed. In the early stage of the improved algorithm, the generalized opposition-based learning strategy is used to make the algorithm quickly close to the potential search area and improve the convergence speed. In the later stage of searching, inspired by evolutionary process of differential evolution, an improved mutation and crossover strategy is developed to update the optimal particle of the current population, thus improving the population diversity and assisting the algorithm to obtain global optimal solution. In order to validate the effectiveness of the improved algorithm, 22 constraint test functions presented in CEC2006 are simulated. Experimental results show that the improved algorithm is superior to other compared algorithms in terms of the accuracy and stability. Finally, the improved algorithm is applied to two economic dispatch problems of power systems, which takes into account the ramp rate limits of the generating units, prohibited operating zones and power balance constraint, and satisfying results are obtained. © 2020, Editorial Office of Control and Decision. All right reserved.
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页码：1813 / 1822
页数：9
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