混合智能算法的多目标无功优化方法
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摘要
传统电力无功优化主要集中在引入或改进某种单一智能优化算法,进化算子的不变性难以保证算法在各寻优阶段的稳定性和普适性.本文提出基于多种智能算法动态混合策略的多目标无功优化方法.该方法采用计及系统网损与电压偏移的多目标优化模型,考虑多种智能算法在不同寻优阶段的优劣特征,基于帕累托最优动态确定备选算法的使用比例,使多种智能算法优势互补以提高整体寻优效率.以IEEE 30节点、系统多目标无功优化为算例,结果表明新方法在帕累托前沿和收敛特性等方面都表现更优.
The traditional optimal reactive power flow(ORPF)is mainly focused on the introduction or improvement of a single intelligent optimization algorithm.The invariance of evolution operator is difficult to ensure the algorithm stability and universality in every optimization phase.A new method for ORPF based on dynamic hybrid strategy of hybrid intelligent algorithm(HIA)is proposed in this paper.By using multipurpose model considering active power loss and voltage offset,and analyzing the advantages and disadvantages of various intelligent algorithms in different optimization stages,hybrid intelligent algorithms can effectively complement each other to improve the overall search efficiency by dynamically determining the usage ratio of alternative algorithms based on Pareto optimality.The comparison test of IEEE 30-bus system reactive power optimization shows that the new method performs better in Pareto fronts and convergence property.
The traditional optimal reactive power flow(ORPF)is mainly focused on the introduction or improvement of a single intelligent optimization algorithm.The invariance of evolution operator is difficult to ensure the algorithm stability and universality in every optimization phase.A new method for ORPF based on dynamic hybrid strategy of hybrid intelligent algorithm(HIA)is proposed in this paper.By using multipurpose model considering active power loss and voltage offset,and analyzing the advantages and disadvantages of various intelligent algorithms in different optimization stages,hybrid intelligent algorithms can effectively complement each other to improve the overall search efficiency by dynamically determining the usage ratio of alternative algorithms based on Pareto optimality.The comparison test of IEEE 30-bus system reactive power optimization shows that the new method performs better in Pareto fronts and convergence property.
引文
[1] He R,Taylor G A,Song Y H.Multi-objective Optimal Reactive Power Flow Including Voltage Security and Demand Profile Classification[J].International Journal of Electrical Power&Energy Systems,2008,30(5):327-336.
[2] Capitanescu F,Wehenkel L.Experiments with the Interior-point Method for Solving Large Scale Optimal Power Flow Problems[J].Electric Power Systems Research,2013,95:276-283.
[3] Salgado R S,Rangel E L.Optimal Power Flow Solutions Through Multi-Objective Programming[J].Energy,2013,42(1):35-45.
[4] Osman M S,Abo-Sinna M A,Mousa A A.A Solution to the Optimal Power Flow Using Genetic Algorithm[J].Applied Mathematics and Computation,2004,155(2):391-405.
[5]刘华臣,王锡淮,肖健梅,等.基于群搜索算法的电力系统无功优化[J].电力系统保护与控制,2014,42(14):93-99.
[6] Sivasubramani S,Swarup KS.Multiagent Based Differential Evolution Approach to Optimal Power Flow[J].Applied Soft Computing,2012,12(2):735-740.
[7] Agrawal S,Dashora Y,Tiwari MK,Son YJ.Interactive Particle Swarm:a Pareto Adaptive Metaheuristic to Multi-Objective Optimization[J].IEEE Trans Syst Man Cybern Part A-Syst.Human 2008;38(2):258-277.
[8] Jeyadevi S,Baskar S,Babulal C K,et al.Solving MultiObjective Optimal Reactive Power Dispatch Using Modified NSGA-II[J].International Journal of Electrical Power&Energy Systems,2011,33(2):219-228.
[9]冯士刚,艾芊.带精英策略的快速非支配排序遗传算法在多目标无功优化中的应用[J].电工技术学报,2007,22(12):146-151.
[10] Hirotaka Yoshida,Kenichi Kawata.A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Security Assessment[J].IEEE Trans on Power Systems,2000,15(4):1232-1239.
[11]J.J.Liang,A.K.Qin,P.N.Suganthan,S.Basker.Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodel Functions[J].IEEE Transactions on Evolutionary Computation.2006,10(3):67-82.
[12]Xiaohui Yuan,Binqiao Zhang,et al.Multi-objective Optimal Power Flow Based on Improved Strength Pareto Evolutionary Algorithm[J].Energy,2017,3(122):70-82.
[13]Zitzler E,Laumanns M,Thiele L.SPEA2:improving the Strength Pareto Evolutionary Algorithm[R].Computer Engineering and Networks Laboratory(TIK),Swiss Federal Institute of Technology(ETH)Zurich;2001.Technology Report No.103.
[14]Varadarajan M,Sworup K S.Solving Multi-objective Optimal Power Flow Using Differential Evolution[J].IET Generation Transmission&Distribution,2008,2(5):720-730.
[15]李鸿鑫,李银红,李智欢.多目标进化算法求解无功优化问题的比较与评估[J].电网技术,2013,37(6):1651-1658.
[2] Capitanescu F,Wehenkel L.Experiments with the Interior-point Method for Solving Large Scale Optimal Power Flow Problems[J].Electric Power Systems Research,2013,95:276-283.
[3] Salgado R S,Rangel E L.Optimal Power Flow Solutions Through Multi-Objective Programming[J].Energy,2013,42(1):35-45.
[4] Osman M S,Abo-Sinna M A,Mousa A A.A Solution to the Optimal Power Flow Using Genetic Algorithm[J].Applied Mathematics and Computation,2004,155(2):391-405.
[5]刘华臣,王锡淮,肖健梅,等.基于群搜索算法的电力系统无功优化[J].电力系统保护与控制,2014,42(14):93-99.
[6] Sivasubramani S,Swarup KS.Multiagent Based Differential Evolution Approach to Optimal Power Flow[J].Applied Soft Computing,2012,12(2):735-740.
[7] Agrawal S,Dashora Y,Tiwari MK,Son YJ.Interactive Particle Swarm:a Pareto Adaptive Metaheuristic to Multi-Objective Optimization[J].IEEE Trans Syst Man Cybern Part A-Syst.Human 2008;38(2):258-277.
[8] Jeyadevi S,Baskar S,Babulal C K,et al.Solving MultiObjective Optimal Reactive Power Dispatch Using Modified NSGA-II[J].International Journal of Electrical Power&Energy Systems,2011,33(2):219-228.
[9]冯士刚,艾芊.带精英策略的快速非支配排序遗传算法在多目标无功优化中的应用[J].电工技术学报,2007,22(12):146-151.
[10] Hirotaka Yoshida,Kenichi Kawata.A Particle Swarm Optimization for Reactive Power and Voltage Control Considering Voltage Security Assessment[J].IEEE Trans on Power Systems,2000,15(4):1232-1239.
[11]J.J.Liang,A.K.Qin,P.N.Suganthan,S.Basker.Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodel Functions[J].IEEE Transactions on Evolutionary Computation.2006,10(3):67-82.
[12]Xiaohui Yuan,Binqiao Zhang,et al.Multi-objective Optimal Power Flow Based on Improved Strength Pareto Evolutionary Algorithm[J].Energy,2017,3(122):70-82.
[13]Zitzler E,Laumanns M,Thiele L.SPEA2:improving the Strength Pareto Evolutionary Algorithm[R].Computer Engineering and Networks Laboratory(TIK),Swiss Federal Institute of Technology(ETH)Zurich;2001.Technology Report No.103.
[14]Varadarajan M,Sworup K S.Solving Multi-objective Optimal Power Flow Using Differential Evolution[J].IET Generation Transmission&Distribution,2008,2(5):720-730.
[15]李鸿鑫,李银红,李智欢.多目标进化算法求解无功优化问题的比较与评估[J].电网技术,2013,37(6):1651-1658.