基于电子搜索算法的电力系统无功优化
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摘要
针对电力系统无功优化过程中,粒子群算法收敛慢以及计算结果容易陷入局部最优的问题,文中利用电子搜索算法代替粒子群算法,以提高计算的收敛速度并使优化计算更容易得到最优解。以网损期望最小为目标,建立了考虑电容器无功补偿和电压器变比的配电网无功优化模型。利用IEEE14节点系统进行模拟计算,通过结果验证了电子搜索算法在无功优化中的效果。通过比较了粒子群算法和电子搜索算法的结果,证明了电子搜索算法在收敛速度以及优化效果上优于粒子群算法。
Aiming at the slow convergence of PSO and the problem that the result of PSO was easy to fall into local solution in the reactive power optimization process of power system,the electro-search algorithm was used instead of PSO to improve the convergence speed and make the optimization calculation easier to get the optimal solution. In order to minimize the expected loss,a reactive power optimization model for distribution network was established,which considered the capacitor reactive power compensation and the voltage transformer ratio. The simulation results of IEEE 14 bus system verified the effectiveness of the electro-search algorithm in reactive power optimization. Finally,the results of particle swarm optimization and electro-search algorithm were compared. The results showed that the electro-search algorithm was superior to particle swarm optimization in convergence speed and optimization effect.
Aiming at the slow convergence of PSO and the problem that the result of PSO was easy to fall into local solution in the reactive power optimization process of power system,the electro-search algorithm was used instead of PSO to improve the convergence speed and make the optimization calculation easier to get the optimal solution. In order to minimize the expected loss,a reactive power optimization model for distribution network was established,which considered the capacitor reactive power compensation and the voltage transformer ratio. The simulation results of IEEE 14 bus system verified the effectiveness of the electro-search algorithm in reactive power optimization. Finally,the results of particle swarm optimization and electro-search algorithm were compared. The results showed that the electro-search algorithm was superior to particle swarm optimization in convergence speed and optimization effect.
引文
[1]刘桂龙,王维庆,张新燕,等.无功优化算法综述[J].电网与清洁能源,2011,27(1):4-8.Liu Guilong,Wang Weiqing,Zhang Xinyan,et al. Overview of VAR optimization algorithm[J]. Power System and Clean Energy,2011,27(1):4-8.
[2]康忠健,訾淑伟.基于差分进化算法的油田区域配电网无功优化技术的研究[J].电工技术学报,2013,28(6):226-231.Kang Zhongjian,Zi Shuwei. Research on the reactive power optimization of oil-field regional distribution network based on differential evolution[J]. Transactions of China Electro Technical Society,2013,28(6):226-231.
[3]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(17):39-43.Ma Ling,Yu Qing,Liu Gang,et al. Power system reactive power optimization based on quantum DE algorithm[J]Power System Protection and Control,2013(17):39-43.
[4]赵昆,耿光飞.基于改进遗传算法的配电网无功优化[J].电力系统保护与控制,2011,39(5):57-62.Zhao Kun,Geng Guangfei. Reactive power optimization of distribution network based on improved genetic algorithm[J].Power System Protection and Control,2011,39(5):57-62.
[5]崔挺,孙元章,徐箭,等.基于改进小生境遗传算法的电力系统无功优化[J].中国电机工程学报,2011,31(19):43-50.Cui Ting,Sun Yuanzhang,Xu Jan,et al. Reactive power optimization of power system based on improved niche genetic algorithm[J]. Proceedings of the CSEE,2011,31(19):43-50.
[6]蒋平,梁乐.基于内点法和遗传算法相结合的交直流系统无功优化[J].高电压技术,2015,41(3):724-729.Jiang Ping,Liang Le. Reactive power optimization of hybrid AC/HVDC power system combining interior point algorithm and genetic algorithm[J]. High Voltage Engineering,2015,41(3):724-729.
[7]王希,王昕,李立学,等.基于动态云进化粒子群算法的风电系统无功优化方法[J].电力系统保护与控制,2013(24):36-43.Wang Xi,Wang Xin,Li Lixue,et al. Reactive power optimization for wind power system based on dynamic cloud evolutionary particle swarm optimization[J]. Power System Protection and Control,2013(24):36-43.
[8]徐俊俊,黄永红,王琪,等.基于自然选择粒子群算法的含DG接入的配电网无功优化[J].电测与仪表,2014,51(10):33-38.Xu Junjun,Huang Yonghong,Wang Qi,et al. Reactive power optimization in distribution network with DG based on natural selection particle swarm optimization[J]. Electrical Measurement&Instrumentation,2014,51(10):33-38.
[9]李娟,杨琳,刘金龙,等.基于自适应混沌粒子群优化算法的多目标无功优化[J].电力系统保护与控制,2011,39(9):26-31.Li Juan,Yang Lin,Liu Jinlong,et al. Multi-objective reactive power optimization based on adaptive chaotic particle swarm optimization[J]. Power System Protection and Control,2011,39(9):26-31.
[10]冷博文.基于遗传算法的电力系统网损最小无功优化研究[D].成都:成都理工大学,2017.Leng Bowen. Research on the optimal reactive power in power system based on the genetic algorithm for the minim power loss[D]. Chengdu:Chengdu University of Technology,2017.
[11]张庭场,耿光飞.基于改进粒子群算法的中压配电网无功优化[J].电网技术,2012,36(2):158-162.Zhang Tingchang,Geng Guangfei. Reactive power optimization for medium voltage distribution network based on improved particle swarm optimization[J]. Power System Technology,2012,36(2):158-162.
[12]刘世成,张建华,刘宗岐.并行自适应粒子群算法在电力系统无功优化中的应用[J].电网技术,2012,36(1):108-112.Liu Shicheng,Zhang Jianhua,Liu Zongqi. Application of parallel adaptive particle swarm optimization algorithm in reactive power optimization of power system[J]. Power System Technology,2012,36(1):108-112.
[13]陈前宇,陈维荣,戴朝华.电力系统无功优化多目标处理与算法改进[J].电力系统保护与控制,2014(5):129-135.Chen Qianyu,Chen Weirong,Dai Chaohua,et al. Reactive power optimization based on modified particle swarm optimization algorithm for power system[J]. Power System Protection and Control,2014(5):129-135.
[14]李佳,刘天琪,李兴源,等.改进粒子群-禁忌搜索算法在多目标无功优化中的应用[J].电力自动化设备,2014,34(8):71-77.Li Jia,Liu Tianqi,Li Xingyuan,et al. Application of improved particle swarm algorithm tabu search algorithm in Multi-Objective reactive power optimization[J]. Electric Power Automation Equipment,2014,34(8):71-77.
[15]曾令全,罗富宝,丁金嫚.禁忌搜索-粒子群算法在无功优化中的应用[J].电网技术,2011,35(7):129-133.Zeng Lingquan,Luo Fubao,Ding Jinman. Application of particle swarm optimization algorithm integrated with tabu search in reactive power optimization[J]. Power System Technology,2011,35(7):129-133.
[16]简献忠,王佳.计及UPFC电力系统无功优化研究[J].电子科技,2015,28(9):50-53.Jian Xianzhong,Wang Jia. Reactive power optimization incorporating unified power flow controller[J]. Electronic Science and Technology,2015,28(9):50-53.
[17] Tabari A,Ahmad A. A new optimization method:ElectroSearch algorithm[J]. Computers&Chemical Engineering,2017(103):1-11.
[2]康忠健,訾淑伟.基于差分进化算法的油田区域配电网无功优化技术的研究[J].电工技术学报,2013,28(6):226-231.Kang Zhongjian,Zi Shuwei. Research on the reactive power optimization of oil-field regional distribution network based on differential evolution[J]. Transactions of China Electro Technical Society,2013,28(6):226-231.
[3]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(17):39-43.Ma Ling,Yu Qing,Liu Gang,et al. Power system reactive power optimization based on quantum DE algorithm[J]Power System Protection and Control,2013(17):39-43.
[4]赵昆,耿光飞.基于改进遗传算法的配电网无功优化[J].电力系统保护与控制,2011,39(5):57-62.Zhao Kun,Geng Guangfei. Reactive power optimization of distribution network based on improved genetic algorithm[J].Power System Protection and Control,2011,39(5):57-62.
[5]崔挺,孙元章,徐箭,等.基于改进小生境遗传算法的电力系统无功优化[J].中国电机工程学报,2011,31(19):43-50.Cui Ting,Sun Yuanzhang,Xu Jan,et al. Reactive power optimization of power system based on improved niche genetic algorithm[J]. Proceedings of the CSEE,2011,31(19):43-50.
[6]蒋平,梁乐.基于内点法和遗传算法相结合的交直流系统无功优化[J].高电压技术,2015,41(3):724-729.Jiang Ping,Liang Le. Reactive power optimization of hybrid AC/HVDC power system combining interior point algorithm and genetic algorithm[J]. High Voltage Engineering,2015,41(3):724-729.
[7]王希,王昕,李立学,等.基于动态云进化粒子群算法的风电系统无功优化方法[J].电力系统保护与控制,2013(24):36-43.Wang Xi,Wang Xin,Li Lixue,et al. Reactive power optimization for wind power system based on dynamic cloud evolutionary particle swarm optimization[J]. Power System Protection and Control,2013(24):36-43.
[8]徐俊俊,黄永红,王琪,等.基于自然选择粒子群算法的含DG接入的配电网无功优化[J].电测与仪表,2014,51(10):33-38.Xu Junjun,Huang Yonghong,Wang Qi,et al. Reactive power optimization in distribution network with DG based on natural selection particle swarm optimization[J]. Electrical Measurement&Instrumentation,2014,51(10):33-38.
[9]李娟,杨琳,刘金龙,等.基于自适应混沌粒子群优化算法的多目标无功优化[J].电力系统保护与控制,2011,39(9):26-31.Li Juan,Yang Lin,Liu Jinlong,et al. Multi-objective reactive power optimization based on adaptive chaotic particle swarm optimization[J]. Power System Protection and Control,2011,39(9):26-31.
[10]冷博文.基于遗传算法的电力系统网损最小无功优化研究[D].成都:成都理工大学,2017.Leng Bowen. Research on the optimal reactive power in power system based on the genetic algorithm for the minim power loss[D]. Chengdu:Chengdu University of Technology,2017.
[11]张庭场,耿光飞.基于改进粒子群算法的中压配电网无功优化[J].电网技术,2012,36(2):158-162.Zhang Tingchang,Geng Guangfei. Reactive power optimization for medium voltage distribution network based on improved particle swarm optimization[J]. Power System Technology,2012,36(2):158-162.
[12]刘世成,张建华,刘宗岐.并行自适应粒子群算法在电力系统无功优化中的应用[J].电网技术,2012,36(1):108-112.Liu Shicheng,Zhang Jianhua,Liu Zongqi. Application of parallel adaptive particle swarm optimization algorithm in reactive power optimization of power system[J]. Power System Technology,2012,36(1):108-112.
[13]陈前宇,陈维荣,戴朝华.电力系统无功优化多目标处理与算法改进[J].电力系统保护与控制,2014(5):129-135.Chen Qianyu,Chen Weirong,Dai Chaohua,et al. Reactive power optimization based on modified particle swarm optimization algorithm for power system[J]. Power System Protection and Control,2014(5):129-135.
[14]李佳,刘天琪,李兴源,等.改进粒子群-禁忌搜索算法在多目标无功优化中的应用[J].电力自动化设备,2014,34(8):71-77.Li Jia,Liu Tianqi,Li Xingyuan,et al. Application of improved particle swarm algorithm tabu search algorithm in Multi-Objective reactive power optimization[J]. Electric Power Automation Equipment,2014,34(8):71-77.
[15]曾令全,罗富宝,丁金嫚.禁忌搜索-粒子群算法在无功优化中的应用[J].电网技术,2011,35(7):129-133.Zeng Lingquan,Luo Fubao,Ding Jinman. Application of particle swarm optimization algorithm integrated with tabu search in reactive power optimization[J]. Power System Technology,2011,35(7):129-133.
[16]简献忠,王佳.计及UPFC电力系统无功优化研究[J].电子科技,2015,28(9):50-53.Jian Xianzhong,Wang Jia. Reactive power optimization incorporating unified power flow controller[J]. Electronic Science and Technology,2015,28(9):50-53.
[17] Tabari A,Ahmad A. A new optimization method:ElectroSearch algorithm[J]. Computers&Chemical Engineering,2017(103):1-11.