基于改进教与学算法的含电能路由器的电力系统无功优化

从帆平; 周建萍; 茅大钧; 齐国庆; 黄祖繁

doi:10.12204/j.issn.1000-7229.2022.06.012

PDF(3217 KB)

电力建设 ›› 2022, Vol. 43 ›› Issue (6) : 110-118. DOI: 10.12204/j.issn.1000-7229.2022.06.012

智能电网

基于改进教与学算法的含电能路由器的电力系统无功优化

作者信息 +

Reactive Power Optimization of Power System with Electric Energy Router Applying Modified Teaching-Learning Algorithm

Author information +

文章历史 +

摘要

针对传统智能算法在求解计及电能路由器的电力系统无功优化模型时存在的收敛性和多样性问题,提出一种基于轮盘赌选择和自适应柯西变异策略的改进教与学算法,并应用于含电能路由器的电力系统无功优化。该算法在学习阶段引入轮盘赌选择法,提高群体的学习效率,在教学完成后引入自适应柯西变异策略,增强班级种群的多样性,避免迭代过程陷入局部最优解。然后,建立以有功网损和电压偏离度最小为目标函数的电力系统无功优化模型,并以修改后的IEEE RTS-79标准测试系统为算例进行仿真分析,结果表明改进后的算法兼顾了收敛性和多样性,相比于传统算法具有更好的优化效果。

Abstract

In this paper,an improved teaching and learning algorithm based on roulette wheel selection and adaptive Corsi variation strategy is proposed to solve the convergence and diversity problems of the traditional intelligent algorithm and it is applied to the reactive power optimization of power system with electric energy router (EER). The algorithm introduces roulette selection method in the learning phase to improve the learning efficiency of the group, and adopts adaptive Cauchy mutation strategy after the teaching to enhance the diversity of the class population, which avoids falling into local optimal solutions in the iterative process. Then, a reactive power optimization model is established with the objective function of minimizing active network loss and voltage deviation. The modified IEEE RTS-79 standard test system is used as an example for simulation analysis. The results show that the improved algorithm has both convergence and diversity, which has better optimization effect than the traditional algorithm.

导出引用

从帆平, 周建萍, 茅大钧, 等. 基于改进教与学算法的含电能路由器的电力系统无功优化[J]. 电力建设. 2022, 43(6): 110-118 https://doi.org/10.12204/j.issn.1000-7229.2022.06.012

Fanping CONG, Jianping ZHOU, Dajun MAO, et al. Reactive Power Optimization of Power System with Electric Energy Router Applying Modified Teaching-Learning Algorithm[J]. Electric Power Construction. 2022, 43(6): 110-118 https://doi.org/10.12204/j.issn.1000-7229.2022.06.012

中图分类号： TM711

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

[1]	SHE X, WANG F, BURGOS R, et al. Solid state transformer interfaced wind energy system with integrated active power transfer, reactive power compensation and voltage conversion functions[C]// 2012 IEEE Energy Conversion Congress and Exposition. IEEE, 2012: 3140-3147. 本文引用 [1]

[2]	SHI J J, GOU W, YUAN H, et al. Research on voltage and power balance control for cascaded modular solid-state transformer[J]. IEEE Transactions on Power Electronics, 2011, 26(4): 1154-1166. 本文引用 [1]

[3]	SHE X, HUANG A. Solid state transformer in the future smart electrical system[C]// 2013 IEEE Power & Energy Society General Meeting. IEEE, 2013: 1-5. 本文引用 [1]

[4]	殷展, 杜仁平, 姜黎明, 等. 基于电能路由器的交直流混合配电网潮流优化控制[J]. 哈尔滨理工大学学报, 2021, 26(4): 20-27. YIN Zhan, DU Renping, JIANG Liming, et al. Power flow optimizaiton control in AC/DC hybrid distribution network based on power router[J]. Journal of Harbin University of Science and Technology, 2021, 26(4): 20-27. 本文引用 [1]

[5]	HUANG L, ZONG C X, YANG X, et al. Power flow calculation of distribution network with multiple energy routers[J]. IEEE Access, 2020, 9: 23489-23497. 本文引用 [1]

[6]	LIU B, WU W H, ZHOU C X, et al. An AC-DC hybrid multi-port energy router with coordinated control and energy management strategies[J]. IEEE Access, 2019, 7: 109069-109082. 本文引用 [1]

[7]	CHEN Y N, WANG P, ELASSER Y, et al. Multicell reconfigurable multi-input multi-output energy router architecture[J]. IEEE Transactions on Power Electronics, 2020, 35(12): 13210-13224. 本文引用 [1]

[8]	LIU Y S, CHEN X, WU Y, et al. Enabling the smart and flexible management of energy prosumers via the energy router with parallel operation mode[J]. IEEE Access, 2020, 8: 35038-35047. 本文引用 [1]

[9]	MIAO J Q, ZHANG N, KANG C Q. Generalized steady-state model for energy router with applications in power flow calculation[C]// 2016 IEEE Power and Energy Society General Meeting. IEEE, 2016: 1-5. 本文引用 [1]

[10]	HUANG L, ZONG C X, YANG X, et al. Power flow calculation of distribution network with multiple energy routers[J]. IEEE Access, 2020, 9: 23489-23497. 本文引用 [1]

[11]	郭靖, 李可军, 王景山, 等. 含能源路由器的交直流网络潮流计算模型及可行解求取[J]. 电力系统自动化, 2018, 42(13): 85-93. GUO Jing, LI Kejun, WANG Jingshan, et al. Power flow calculation model and feasible solution of AC-DC network with energy routers[J]. Automation of Electric Power Systems, 2018, 42(13): 85-93. 本文引用 [1]

[12]

张晓英, 张艺, 王琨, 等. 基于改进NSGA-Ⅱ算法的含分布式电源配电网无功优化[J]. 电力系统保护与控制, 2020, 48(1): 55-64.

ZHANG

Xiaoying

, ZHANG

, WANG

Kun

, et al. Reactive power optimization of distribution network with distributed generations based on improved NSGA-Ⅱ algorithm[J]. Power System Protection and Control, 2020, 48(1): 55-64.

本文引用 [1]

[13]	陈国发, 张文庆, 刘锋, 等. 基于改进蜂群算法的含DG配网多目标无功优化[J]. 智慧电力, 2019, 47(3): 97-103. CHEN Guofa, ZHANG Wenqing, LIU Feng, et al. Multi-objective reactive power optimization of distribution network with DG based on improved bee colony algorithm[J]. Smart Power, 2019, 47(3): 97-103. 本文引用 [1]

[14]	杨蕾, 吴琛, 黄伟, 等. 含高比例风光新能源电网的多目标无功优化算法[J]. 电力建设, 2020, 41(7): 100-109. YANG Lei, WU Chen, HUANG Wei, et al. Pareto-based multi-objective reactive power optimization for power grid with high-penetration wind and solar renewable energies[J]. Electric Power Construction, 2020, 41(7): 100-109. 本文引用 [1]

[15]	QU X H, LIU B, LI Z Y, et al. A novel improved teaching-learning based optimization for functional optimization[C]// 2016 12th IEEE International Conference on Control and Automation.IEEE, 2016: 939-943. 本文引用 [1]

[16]	ZHAI J C, QIN Y P, ZHAO Z. A self-reflection teaching-learning-based optimization algorithm and its application[C]// 2019 Chinese Control and Decision Conference (CCDC). IEEE, 2019: 1446-1451. 本文引用 [1]

[17]	童楠, 符强, 钟才明. 基于自主学习行为的教与学优化算法[J]. 计算机应用, 2018, 38(2): 443-447. TONG Nan, FU Qiang, ZHONG Caiming. Improved teaching-learning-based optimization algorithm based on self-learning mechanism[J]. Journal of Computer Applications, 2018, 38(2): 443-447. 本文引用 [1]

[18]	CHEN Y P, LI S Z, LIN X M. Fast hog feature computation based on CUDA[C]// 2011 IEEE International Conference on Computer Science and Automation Engineering. IEEE, 2011: 748-751. 本文引用 [1]

[19]	HIRABAYASHI M, KATO S, EDAHIRO M, et al. GPU implementations of object detection using HOG features and deformable models[C]// 2013 IEEE 1st International Conference on Cyber-Physical Systems, Networks, and Applications. IEEE, 2013: 106-111. 本文引用 [1]

[20]	MIAO J Q, ZHANG N, KANG C Q, et al. Steady-state power flow model of energy router embedded AC network and its application in optimizing power system operation[J]. IEEE Transactions on Smart Grid, 2018, 9(5): 4828-4837. 本文引用 [1]

[21]	郑超, 周孝信, 李若梅, 等. VSC-HVDC稳态特性与潮流算法的研究[J]. 中国电机工程学报, 2005, 25(6): 1-5. ZHENG Chao, ZHOU Xiaoxin, LI Ruomei, et al. Study on the steady characteristic and algorithm of power flow for VSC-HVDC[J]. Proceedings of the CSEE, 2005, 25(6): 1-5. 本文引用 [1]

[22]

李红伟, 蒋嘉焱, 刘青卓, 等. 基于改进教与学算法的配网多目标无功优化[J]. 控制工程, 2020, 27(5): 878-883.

Hongwei

, JIANG

Jiayan

, LIU

Qingzhuo

, et al. Multi-objective reactive power optimization of distribution network based on improved teachIng learning based optimization[J]. Control Engineering of China, 2020, 27(5): 878-883.

本文引用 [1]

[23]	GRIGG C, WONG P, ALBRECHT P, et al. The IEEE reliability test system-1996: A report prepared by the reliability test system task force of the application of probability methods subcommittee[J]. IEEE Transactions on Power Systems, 1999, 14(3): 1010-1020. 本文引用 [1]