基于改进差分进化和粒子群混合算法的电力系统最优潮流计算

陈璟华; 邱明晋; 唐俊杰; 田明正; 谭耿锐

doi:10.12052/gdutxb.160149

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基于改进差分进化和粒子群混合算法的电力系统最优潮流计算

综合研究 | 更新时间：2026-05-27

- 基于改进差分进化和粒子群混合算法的电力系统最优潮流计算
- A Hybrid Algorithm Based on Improved Differential Evolution and Particle Swarm Optimization for Power System Optimal Power Flow Calculation
- 广东工业大学学报 2017年34卷第5期页码：22-28
- 作者机构：
- 作者简介：
  
  陈璟华(1974-),女,副教授,博士,主要研究方向为电力系统运行与优化.
- 基金信息：
  
  广东省自然科学基金资助项目（S2013040013776）;广东省教育厅育苗工程项目（2013LYM_0019）
- DOI：10.12052/gdutxb.160149
  中图分类号： TM744
- 网络首发：2017-09-09，
  
  纸质出版：2017
- 稿件说明：
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陈璟华, 邱明晋, 唐俊杰, 田明正, 谭耿锐. 基于改进差分进化和粒子群混合算法的电力系统最优潮流计算[J]. 广东工业大学学报, 2017, 34(5): 22-28.

Chen Jing-hua, Qiu Ming-jin, Tang Jun-jie, et al. A Hybrid Algorithm Based on Improved Differential Evolution and Particle Swarm Optimization for Power System Optimal Power Flow Calculation[J]. 2017, 34(5): 22-28.
陈璟华, 邱明晋, 唐俊杰, 田明正, 谭耿锐. 基于改进差分进化和粒子群混合算法的电力系统最优潮流计算[J]. 广东工业大学学报, 2017, 34(5): 22-28. DOI： 10.12052/gdutxb.160149.

Chen Jing-hua, Qiu Ming-jin, Tang Jun-jie, et al. A Hybrid Algorithm Based on Improved Differential Evolution and Particle Swarm Optimization for Power System Optimal Power Flow Calculation[J]. 2017, 34(5): 22-28. DOI： 10.12052/gdutxb.160149.

摘要

针对电力系统最优潮流典型的非线性多峰值的非凸规划问题，提出一种将差分进化与粒子群优化算法结合在一起的混合优化算法.采用双种群进化策略，分别利用粒子群优化算法和差分进化算法进行寻优迭代，通过信息分享机制，使两个种群在寻优过程中协同进化.提出一种老化机制和精英改选机制，根据最优粒子的引导能力动态改变其寿命.在其引导能力不足时，采用一种多项式变异策略引入一个竞争个体与最优粒子竞争，使算法全局寻优能力得到加强.IEEE30节点系统仿真结果表明，算法收敛速度快、精度高，具有一定的有效性和可行性.

Abstract

A hybrid optimization algorithm combining differential evolution and particle swarm optimization is proposed to solve the non-convex programming problem of nonlinear optimal multi-peak of power system optimal power flow. By using the two-species evolutionary strategy

the particle swarm optimization algorithm and the differential evolution algorithm are used to optimize iterations. The information sharing mechanism is used to co-evolve the two populations in the process of optimization. An aging mechanism and a mechanism of elite selection are proposed to dynamically change the lifetime of the particle according to its guiding ability. In the case of lack of bootstrap capability

a polynomial mutation strategy is adopted to produce a competitor to compete with the optimal particle

so that the global optimization ability of the algorithm is enhanced. Simulation results show that the algorithm has high convergence speed and high precision

and it is effective and feasible.

关键词

Keywords

references

俞俊霞, 赵波. 基于改进粒子群优化算法的最优潮流计算[J]. 电力系统及其自动化学报, 2005, 17(4):83-88.YU J X, ZHAO B. Improved particle swam optimization algorithm for optimal power flow problems[J]. Proceedings of the CSU-EPSA, 2005, 17(4):83-88.

DOMMEL H W, TINNEY W F. Optimal power flow solutions[J]. IEEE Trans on Power Apparatus and Systems, 1968, 87(10):1866-1876.

陈恳. 直角坐标牛顿-拉夫逊法潮流计算新解法[J]. 电力系统及其自动化学报, 1999, 11(4):66-69.CHEN K. New power flow solution for rectangular coordinate Newton-Raphson[J]. Proceedings of the CSU-EPSA, 1999, 11(4):66-69.

杨跃光, 刘璇. 一种改进的Newton算法在电力系统潮流计算中的研究与应用[J]. 陕西电力, 2011, 39(10):4-6.YANG Y G, LIU X. Research and application of improved newton algorithm for power flow calculation in power system[J]. Shaanxi Electric Power, 2011, 39(10):4-6.

候芳, 吴政球, 王良缘. 基于内点法的快速解耦最优潮流算法[J]. 电力系统及其自动化学报, 2001, 13(6):8-12.HOU F, WU Z Q, WANG L Y. Optimal power solution based on fast decouple interior point algorithm[J]. Proceedings of the CSU-EPSA, 2001, 13(6):8-12.

蔡广林, 韦化. 基于非线性互补方法的内点最优潮流算法[J]. 电网技术, 2005, 29(21):21-26.CAI G L, WEI H. An interior point optimal power flow algorithm based on nonlinear complementarity method[J]. Power System Technology, 2005, 29(21):21-26.

李鑫滨, 朱庆军. 一种改进粒子群优化算法在多目标无功优化中的应用[J]. 电工技术学报, 2010, 25(7):137-143.LI X B, ZHU Q J. Application of improved particle swarm optimization algorithm to multi-objective reactive power optimization[J]. Transactions of China Electric Technical Society, 2010, 25(7):137-143.

PRICE K, STORN R. Differential evolution:a simple evolution strategy for fast optimization[J]. Software Tools, 1997, 22(4):18-24.

罗萍, 刘伟, 周述波. 自适应混沌变异的万有引力搜索算法[J]. 广东工业大学学报, 2016, 33(01):57-61.LUO P, LIU W, ZHOU S B. Gravitational search algorithm of adaptive chaos mutation[J]. Journal of Guangdong University of Technology, 2016, 33(01):57-61.

乐秀璠, 覃振成, 尹峰. 基于自适应模拟退火遗传算法的多目标最优潮流[J]. 继电器, 2005, 33(7):10-15.LIE X F, QIN Z C, YIN F. Multi-objective optimal power flow based on adaptive simulated annealing genetic algorithm[J]. Relay, 2005, 33(7):10-15.

陈璟华, 梁丽丽, 丁林军, 等. 计及风电场并网的机会约束规划的机组组合优化[J]. 广东工业大学学报, 2017, 34(01):50-54, 64.CHEN J H, LIANG L L, DING L J, et al. Unit commitment optimization based on chance-constrained programming in wind power integrated system[J]. Journal of Guangdong University of Technology, 2017, 34(01):50-54, 64.

马玲, 于青, 刘刚, 等. 基于量子差分进化算法的电力系统无功优化[J]. 电力系统保护与控制, 2013, 41(17):39-43.MA L, YU Q, LIU G, et al. Power system reactive power optimization based on quantum DE algorithm[J]. Power System Protection and Control, 2013, 41(17):39-43.

郭经韬, 陈璟华, 周俊, 等. 基于组合混沌序列动态粒子群算法的电力系统无功优化[J]. 广东工业大学学报, 2014, 31(2):85-89.GUO J T, CHEN J H, ZHOU J, et al. Reactive power optimization based on combined chaotic dynamic particle swarm optimization algorithm[J]. Journal of Guangdong University of Technology, 2014, 31(2):85-89.

阳春华, 钱晓山, 桂卫华. 一种混沌差分进化和粒子群优化混合算法[J]. 计算机应用研究, 2011, 28(2):439-441.YANG C H, QIAN X S, GUI W H. Hybrid algorithm of chaotic differential evolution and particle swarm optimization[J]. Application Research of Computers, 2011, 28(2):439-441.

DEB K. Multi-objective Optimization Using Evolutionary Algorithms[M]. New Jersey:John Wiley & Sons, Inc, 2001.

ALSAC O, STOTT B. Optimal load flow with steady-state security[J]. IEEE transactions on power apparatus and systems, 1974,(3):745-751.

YURYEVICH J, WONG K P. Evolutionary programming based optimal power flow algorithm[J]. IEEE Transactions on Power Systems, 1999, 14(4):1245-1250.

SAYAH S, ZEHAR K. Modified differential evolution algorithm for optimal power flow with non-smooth cost functions[J]. Energy conversion and Management, 2008, 49(11):3036-3042.

LAI L L, MA J T, YOKOYAMA R, et al. Improved genetic algorithms for optimal power flow under both normal and contingent operation states[J]. International Journal of Electrical Power & Energy Systems, 1997, 19(5):287-292.

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