基于鲸鱼优化算法的无功优化调度
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摘要
针对目前电力系统中的无功优化问题尚缺乏一种能兼顾求解的高效性与全局搜索最优性的方法,文中将一种新的启发式算法-鲸鱼优化算法(WOA)运用到电网无功优化调度中,以系统有功功率损耗最低为目标函数,通过引入惩罚函数建立无功优化模型,对IEEE-14节点系统与IEEE-30节点系统进行仿真,并利用单因素方差分析法(One-way ANOVA)将所得结果与之前的粒子群优化算法(PSO)及引入加速度系数的时变粒子群优化(PSOTVAC)进行比较,研究表明WOA算法在迭代次数、搜索能力及收敛问题上的潜力,并证明了在解决电力系统无功优化问题上的鲁棒性和有效性,同时也为解决非线性约束问题提供了新途径。
In order to solve the problem of reactive power optimization in power system,which is still lack of a method that taking into account both the efficiency of optimization and the global searching optimality. In this paper,a new heuristic algorithm called whale optimization algorithm( WOA) is applied to grid reactive power optimization,which takes the lowest active power loss of the system as the objective function,and a reactive power optimization model is established by introducing a penalty function to simulate the IEEE 14-bus system and the IEEE 30-bus system. One-way ANOVA is used to compare the results of particle swarm optimization( PSO) algorithm and the particle swarm optimization with time varying acceleration coefficients( PSO-TVAC). The results show that the WOA algorithm has better performance in iteration times.It also proves the robustness and effectiveness on resolving the reactive power optimization problem of power system,and provides a new way to solve the nonlinear constraint problem.
In order to solve the problem of reactive power optimization in power system,which is still lack of a method that taking into account both the efficiency of optimization and the global searching optimality. In this paper,a new heuristic algorithm called whale optimization algorithm( WOA) is applied to grid reactive power optimization,which takes the lowest active power loss of the system as the objective function,and a reactive power optimization model is established by introducing a penalty function to simulate the IEEE 14-bus system and the IEEE 30-bus system. One-way ANOVA is used to compare the results of particle swarm optimization( PSO) algorithm and the particle swarm optimization with time varying acceleration coefficients( PSO-TVAC). The results show that the WOA algorithm has better performance in iteration times.It also proves the robustness and effectiveness on resolving the reactive power optimization problem of power system,and provides a new way to solve the nonlinear constraint problem.
引文
[1]林济铿,石伟钊,吴乃虎,等.计及离散变量基于互补约束全光滑牛顿法的无功优化[J].中国电机工程学报,2012,32(1):93-100.Lin Jikeng, Shi Weizhao, Wu Naihu, et al. Reactive Power Optimization With Discrete Variables Based on Complementarity Constraints Smooth Newton Method[J].Proceedings of the CSEE,2012,32(1):93-100.
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[10]黄华,吴耀武,等.基于Box算法的无功优化配置[J].电力系统自动化,2000,(20):32-37.
[11]陈功贵,李智欢,陈金富,等.含风电场电力系统动态优化潮流的混合蛙跳算法[J].电力系统自动化,2009,33(4):25-30.
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[13]M.M.Mafarja,S. Mirjalili,Hybrid Whale Optimization Algorithm with simulated annealing for feature selection[J]. Neurocomputing,2017,260:302-312.
[14]M.Ghasemi,et al. A new hybrid algorithm for optimal reactive power dispatch problem with discrete and continuous control variables[J].Appl. Soft Comput,2014,22:126-140.
[15]杨丽徙,王锴,程杰.应用改进模拟植物生长算法求解无功优化问题[J].高电压技术,2009,(3):694-698.
[16]牛培峰,吴志良,马云鹏,等.基于鲸鱼优化算法的汽轮机热耗率模型预测[J].化工学报,2017,68(3):1049-1057.Niu Peifeng,Wu Zhiliang,Ma Yunpeng,et al. Prediction of steam turbine heat consumption rate based on whale optimization algorithm[J].CIESC Journal,2017,68(3):1049-1057.
[17]徐继亚,王艳,纪志成.基于鲸鱼优化算法WKELM的滚动轴承故障诊断[J].系统仿真学报,2017,29(9):2189-2197.Xu Jiya,Wang Yan,Ji Zhicheng. Fault Diagnosis Method of Rolling Bearing Based on WKELM Optimized by Whale Optimization Algorithm[J],Journal of System Simulation,2017,29(9):2189-2197.
[18]谢建群,刘怡俊,李生.改进鲸鱼算法在云计算资源负载预测中的应用[J].计算机工程与应用,2018,54(13):73-77.Xie Jianqun,Liu Yijun,Li Sheng. Application of improved whale algorithm in load forecasting of cloud computing resources[J]. Computer Engineering and Applications,2018,54(13):73-77.
[19]崔东文.鲸鱼优化算法在水库优化调度中的应用[J].水利水电科技进展,2017,37(3):72-76.
[20]S.Mirjalili,A. Lewis,The whale optimization algorithm[J]. Adv. Eng.Softw. 2016,95:51-67.
[21]Y.Amrane,et al. Optimal VAR control for real power loss minimization using differential evolution algorithm[J]. Int. J. Electr. Power Energy Syst. 2015,66:262-271.
[22]A. Rajan,T. Malakar,Optimal reactive power dispatch using hybrid Nelder-Mead simplex based firefly algorithm[J]. Electr. Power Energy Syst,2015,66:9-24.
[23]S.García,J. Luengo,F. Herrera,Data Preprocessing in Data Mining[J]. Springer,2015.
[2]谷永刚,肖凯,夏经德,等.利用增量二次规划和启发式方法的电力系统动态无功优化[J].西安交通大学学报,2010,44(8):106-111.Gu Yonggang,Xiao Kai,Xia Jingde,et al. Incremental Quadratic Programming and Heuristic Combination Algorithm for Dynamic Optimal Reactive Power in Power System[J]. Journal of Xi'an Jiaotong University,2010,44(8):106-111.
[3]姚煜,蔡燕春.离散粒子群与内点法结合的电力系统无功优化[J].电力系统保护与控制,2010,38(3):48-52.Yao Yu,Cai Yanchun. A hybrid strategy based on DPSO and IPM for optimal reactive power flow[J]. Power System Protection and Control,2010,38(3):48-52.
[4]张永平,童小娇,吴复立,等.基于非线性互补问题函数的半光滑牛顿最优潮流算法[J].中国电机工程学报,2004,9(9):130-135.Zhang Yongping,Tong Xiaojiao,Wu Fuli,et al. Study on semismooth newton optimal power flow algorithm based on nonlinear complementarity problem function[J]. Proceedings of the CSEE,2004,9(9):130-135.
[5]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.
[6] A.Ghasemi,et al.,Multi objective optimal reactive power dispatch using a new multi objective strategy[J]. Int. J. Electr. Power Energy Syst,2014,(57):318-334.
[7]张丰田,宋家骅,李鉴,等.基于混合差分进化算法的电力系统无功优化[J].电网技术,2007,31(9):33-37.
[8]林娇燕.基于蚁群算法的无功优化方法的研究[J].低压电器,2011,(20):43-46.Lin Jiaoyan. Study on Reactive Power Optimization Based on Ant Colony Algorithm[J]. Low Voltage Apparatus,2011,(20):43-46.
[9] K.Ayan,U. K 9,Artificial bee colony algorithm solution for optimal reactive power flow[J]. Appl. Soft Comput. 2012,(12):1477-1482.
[10]黄华,吴耀武,等.基于Box算法的无功优化配置[J].电力系统自动化,2000,(20):32-37.
[11]陈功贵,李智欢,陈金富,等.含风电场电力系统动态优化潮流的混合蛙跳算法[J].电力系统自动化,2009,33(4):25-30.
[12]S.Duman,et al. Optimal power flow using gravitational search algorithm[J]. Energy Convers. Manag. 2012,(59):86-95.
[13]M.M.Mafarja,S. Mirjalili,Hybrid Whale Optimization Algorithm with simulated annealing for feature selection[J]. Neurocomputing,2017,260:302-312.
[14]M.Ghasemi,et al. A new hybrid algorithm for optimal reactive power dispatch problem with discrete and continuous control variables[J].Appl. Soft Comput,2014,22:126-140.
[15]杨丽徙,王锴,程杰.应用改进模拟植物生长算法求解无功优化问题[J].高电压技术,2009,(3):694-698.
[16]牛培峰,吴志良,马云鹏,等.基于鲸鱼优化算法的汽轮机热耗率模型预测[J].化工学报,2017,68(3):1049-1057.Niu Peifeng,Wu Zhiliang,Ma Yunpeng,et al. Prediction of steam turbine heat consumption rate based on whale optimization algorithm[J].CIESC Journal,2017,68(3):1049-1057.
[17]徐继亚,王艳,纪志成.基于鲸鱼优化算法WKELM的滚动轴承故障诊断[J].系统仿真学报,2017,29(9):2189-2197.Xu Jiya,Wang Yan,Ji Zhicheng. Fault Diagnosis Method of Rolling Bearing Based on WKELM Optimized by Whale Optimization Algorithm[J],Journal of System Simulation,2017,29(9):2189-2197.
[18]谢建群,刘怡俊,李生.改进鲸鱼算法在云计算资源负载预测中的应用[J].计算机工程与应用,2018,54(13):73-77.Xie Jianqun,Liu Yijun,Li Sheng. Application of improved whale algorithm in load forecasting of cloud computing resources[J]. Computer Engineering and Applications,2018,54(13):73-77.
[19]崔东文.鲸鱼优化算法在水库优化调度中的应用[J].水利水电科技进展,2017,37(3):72-76.
[20]S.Mirjalili,A. Lewis,The whale optimization algorithm[J]. Adv. Eng.Softw. 2016,95:51-67.
[21]Y.Amrane,et al. Optimal VAR control for real power loss minimization using differential evolution algorithm[J]. Int. J. Electr. Power Energy Syst. 2015,66:262-271.
[22]A. Rajan,T. Malakar,Optimal reactive power dispatch using hybrid Nelder-Mead simplex based firefly algorithm[J]. Electr. Power Energy Syst,2015,66:9-24.
[23]S.García,J. Luengo,F. Herrera,Data Preprocessing in Data Mining[J]. Springer,2015.