基于需求差异化的电网核心骨干网架构建
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摘要
为了提高故障状态下电网保障重要负荷的能力,提出一种基于需求差异化的核心骨干网架构建方法。分别从负荷、电源和网架三个需求方面建立了核心骨干网架的数学模型,同时改进了网架连通性修复策略。采用引入了动态旋转角,早熟判断机制和混沌变异策略的改进量子粒子群算法进行模型的求解,并与量子粒子群算法和量子进化算法的搜索结果进行了对比分析。应用IEEE-118节点系统进行算例分析,结果表明能准确搜索出基于负荷、电源和网架需求的核心骨干网架,所提出的改进量子粒子群算法收敛快,能够克服陷入局部最优,收敛精度高。
In order to improve the ability to protect the important load under the fault status,a construction method based on the needs differentiation of the core backbone network is presented. The mathematic models of core backbone network are established from three aspects of loads,sources and grids needs,and the network connectivity-repairing strategy is improved. The improved QBPSO is applied to solve the model with dynamic rotation angle,premature judgment mechanism and chaotic mutation strategy. Search results are compared and analyzed with quantum binary particle swarm algorithm( QBPSO) and quantum-inspired evolutionary algorithm( QEA). It is applied to the IEEE-118 system,and the results show that the method can accurately search the core backbone network based on loads,sources and grids needs. The proposed improved QBPSO has fast convergence and better optimum result,which can overcome the local optimum.
In order to improve the ability to protect the important load under the fault status,a construction method based on the needs differentiation of the core backbone network is presented. The mathematic models of core backbone network are established from three aspects of loads,sources and grids needs,and the network connectivity-repairing strategy is improved. The improved QBPSO is applied to solve the model with dynamic rotation angle,premature judgment mechanism and chaotic mutation strategy. Search results are compared and analyzed with quantum binary particle swarm algorithm( QBPSO) and quantum-inspired evolutionary algorithm( QEA). It is applied to the IEEE-118 system,and the results show that the method can accurately search the core backbone network based on loads,sources and grids needs. The proposed improved QBPSO has fast convergence and better optimum result,which can overcome the local optimum.
引文
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[15]吴小珊,张步涵,袁小明,等.求解含风电场的电力系统机组组合问题的改进量子离散粒子群优化方法[J].中国电机工程学报,2013,33(4):45-52.Wu Xiaoshan,Zhang Buhan,Yuan Xiaoming,et al.Solutions to Unit Commitment Problems in Power Systems With Wind Farms Using Advanced Quantum-inspired Binary PSO[J].Proceedings of the CSEE,2013,33(4):45-52.
[2]钟慧荣.电力系统黑启动与网架重构优化技术研究[D].华北电力大学,2012.
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[4]杨文辉,毕天姝,黄少锋,等.基于电网生存性评估的关键线路识别方法[J].中国电机工程学报,2011,(7):29-35.
[5]刘艳,顾雪平.基于节点重要度评价的骨架网络重构[J].中国电机工程学报,2007,27(10):20-27.Liu Yan,Gu Xueping.Node Importance Assessment Based Skeletonnetwork Reconfiguration[J].Proceedings of the CSEE,2007,27(10):20-27.
[6]魏智博,刘艳,顾雪平.基于DPSO算法以负荷恢复为目标的网络重构[J].电力系统自动化,2007,31(1):38-42.Wei Zhibo,Liu Yan,Gu Xueping.DPSO Algorithm Based Network Reconfiguration of Power Systems for Maximizing Load Recovery Efficiency[J].Automation of Electric Power Systems,2007,31(1):38-42.
[7]王浩磊,刘涤尘,吴军,等.基于改进二进制量子粒子群算法的核心骨干网架搜索[J].中国电机工程学报,2014,(34):6127-6133.Wang Haolei,Liu Dichen,Wu Jun,et al.Core Backbone Network Searching Based on Improved Quantum Binary Particle Swarm Optimization[J].Proceedings of the CSEE,2014,(34):6127-6133.
[8]董飞飞,刘涤尘,吴军,等.基于改进BBO优化算法和电网生存性的核心骨干网架构建[J].中国电机工程学报,2014,34(16):2659-2667.
[9]Jeong Y W,Park J B,Jang S H,et al.A New Quantum-Inspired Binary PSO for Thermal Unit Commitment Problems[C]//International Conference on Intelligent System Applications To Power Systems.IEEE,2009:1-6.
[10]Lau T W,Chung C Y,Wong K P,et al.Quantum-Inspired Evolutionary Algorithm Approach for Unit Commitment[J].IEEE Transactions on Power Systems,2009,24(3):1503-1512.
[11]罗日成,李卫国.基于图论的配电网电气连通性分析算法[J].电工技术学报,2005,20(10):98-102.
[12]Kennedy J,Eberhart R.Particle swarm optimization[C]//IEEE International Conference on Neural Networks,1995,(4):1942-1948.
[13]Kennedy J,Eberhart R C.A discrete binary version of the particle swarm algorithm[J].1997,(5):4104-4108.
[14]林星,冯斌,孙俊.混沌量子粒子群优化算法[J].计算机工程与设计,2008,29(10):2610-2612.
[15]吴小珊,张步涵,袁小明,等.求解含风电场的电力系统机组组合问题的改进量子离散粒子群优化方法[J].中国电机工程学报,2013,33(4):45-52.Wu Xiaoshan,Zhang Buhan,Yuan Xiaoming,et al.Solutions to Unit Commitment Problems in Power Systems With Wind Farms Using Advanced Quantum-inspired Binary PSO[J].Proceedings of the CSEE,2013,33(4):45-52.