基于柯西变异改进粒子群算法的无功优化 *

doi:10.11985/2021.01.008

电气工程学报 ›› 2021, Vol. 16 ›› Issue (1): 55-61.doi: 10.11985/2021.01.008

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基于柯西变异改进粒子群算法的无功优化 ^*

苏福清¹(), 匡洪海¹, 钟浩²(), 匡威¹(), 陶成¹

1.湖南工业大学电气与信息工程学院株洲 412007
2.三峡大学梯级水电站运行与控制湖北省重点实验室宜昌 443002

收稿日期:2020-08-08 修回日期:2021-01-18 出版日期:2021-03-25 发布日期:2021-03-25
通讯作者: 匡威 E-mail:su_fuqing@163.com;zhonghao022@163.com;khhzyz@163.com
作者简介:匡洪海,女,1972年生,博士,教授。主要研究方向为分布式发电技术和配电网停电管理。E-mail：khhzyz@163.com
苏福清,男,1995年生,硕士研究生。主要研究方向为电力系统优化运行与控制。E-mail：su_fuqing@163.com
钟浩,男,1983年生,博士,副教授。主要研究方向为电力系统运行与控制,梯级水电站运行与控制。E-mail：zhonghao022@163.com
基金资助:
* 湖南省自然科学基金(2018JJ4076);湖北省重点实验室开放基金资助项目(2019KJX06)

Reactive Power Optimization Based on Cauchy Mutation and Improved Adaptive Particle Swarm Optimization

SU Fuqing¹(), KUANG Honghai¹, ZHONG Hao²(), KUANG Wei¹(), TAO Cheng¹

1. College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007
2. Hubei Key Laboratory of Cascaded Hydropower Stations Operation & Control, China Three Gorges University, Yichang 443002

Received:2020-08-08 Revised:2021-01-18 Online:2021-03-25 Published:2021-03-25
Contact: KUANG Wei E-mail:su_fuqing@163.com;zhonghao022@163.com;khhzyz@163.com

摘要/Abstract

摘要：

针对粒子群算法用于无功优化问题求解时存在早熟收敛,易陷入局部最优的现象,提出了基于柯西变异的自适应混沌粒子群算法。该算法在引入自适应调整策略和对最佳粒子采用混沌搜索的基础上,对算法陷入早熟收敛状态时引入柯西变异操作,将适应度值排名位于前20%的最优粒子进行柯西扰动,以保证粒子群的多样性,有效地提高了算法后期跳出局部最优解的能力。以有功网损为目标函数,并入电压和无功出力约束的惩罚函数项,对IEEE 14和IEEE 30节点标准算例进行仿真计算,验证了该算法的正确性和可行性。

关键词: 混沌搜索, 粒子群优化算法, 柯西变异, 无功优化

Abstract:

Aiming at the phenomenon of premature convergence and easy to fall into local optimum when the particle swarm algorithm is used to solve reactive power optimization problems, an adaptive chaotic particle swarm algorithm based on Cauchy mutation is proposed. Based on the introduction of adaptive adjustment strategies and chaotic search for the best particles, Cauchy mutation operation is led in when the algorithm falls into a premature convergence state, and performs Cauchy perturbation on the best particles with fitness values ranked in the top 20% to ensure the diversity of particle swarms, thus the ability of the algorithm to jump out of the local optimal solution is enhanced effectively in the later stage. Taking the active power loss as the objective function and incorporating the penalty function terms of the voltage and reactive power output constraints, the simulation calculations of IEEE 14 and IEEE 30 node standard examples have verified the correctness and feasibility of the algorithm.

Key words: Chaotic search, particle swarm optimization, Cauchy mutation, reactive power optimization

中图分类号:

TM711

苏福清, 匡洪海, 钟浩, 匡威, 陶成. 基于柯西变异改进粒子群算法的无功优化 ^*[J]. 电气工程学报, 2021, 16(1): 55-61.

SU Fuqing, KUANG Honghai, ZHONG Hao, KUANG Wei, TAO Cheng. Reactive Power Optimization Based on Cauchy Mutation and Improved Adaptive Particle Swarm Optimization[J]. Journal of Electrical Engineering, 2021, 16(1): 55-61.

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	PSO	ACPSO	IA-CPSO
V_G2	1.086	1.086	1.086
V_G3	1.057	1.057	1.057
V_G4	1.100	1.100	1.100
V_G5	1.100	1.100	1.089
K_T1	1.025	1.025	0.950
K_T2	0.900	0.900	1.050
K_T3	1.000	1.000	1.000
Q_C1	0.200	0.200	0.300

	PSO	ACPSO	IA-CPSO
V_G2	1.086	1.086	1.084
V_G3	1.055	1.054	1.052
V_G4	1.061	1.060	1.058
V_G5	1.100	1.100	1.079
V_G6	1.100	1.100	1.100
K_T1	0.975	1.100	1.025
K_T2	1.100	0.900	1.025
K_T3	1.075	1.100	0.975
K_T4	1.075	1.000	0.950
Q_C1	0	0	0.400
Q_C2	0.100	0.100	0.100

	有功网损/p.u.	降损率(%)
初始潮流	0.133 930	?
PSO	0.122 657	8.42
ACPSO	0.122 657	8.42
IA-CPSO	0.122 650	8.42

	有功网损/p.u.	降损率(%)
初始潮流	0.175 57	?
PSO	0.160 841	8.39
ACPSO	0.160 612	8.52
IA-CPSO	0.159 815	8.97

基于柯西变异改进粒子群算法的无功优化 ^*

Reactive Power Optimization Based on Cauchy Mutation and Improved Adaptive Particle Swarm Optimization

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