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基于改进匹配潮流技术的配电网虚拟量测
引用本文:赵冬梅,邱辰,张旭.基于改进匹配潮流技术的配电网虚拟量测[J].现代电力,2012,29(5):37-41.
作者姓名:赵冬梅  邱辰  张旭
作者单位:华北电力大学,北京,102206
摘    要:针对缺少冗余量测的配电网系统,为了简化状态估计方法、保证计算精度,优化配电网络状态,基于匹配潮流思想提出了配电网虚拟量测的概念,建立了以电压量测自适应抗差加权最小二乘为目标函数、以网络潮流匹配和功率失配量分配参数方程为约束条件的虚拟量测数学模型,给出了引入混沌优化、强引导机制的改进粒子群优化求解方法,并详细介绍了配电网虚拟量测的应用流程。采用IEEE8、IEEE33、IEEE69节点系统对该方法进行测试,结果表明,方法可在较少的迭代次数内获得较为准确的配电网状态,且不涉及量测变换、矩阵计算,具有良好的应用前景。

关 键 词:配电网  虚拟量测  匹配潮流  混沌  强引导机制  自适应抗差  粒子群算法

The Virtual Measurement of Distribution Network Based on Improved Matching Power Flow Technique
ZHAO Dongmei , QIU Chen , ZHANG Xu.The Virtual Measurement of Distribution Network Based on Improved Matching Power Flow Technique[J].Modern Electric Power,2012,29(5):37-41.
Authors:ZHAO Dongmei  QIU Chen  ZHANG Xu
Affiliation:(North China Electric Power University,Beijing 102206,China)
Abstract:For distribution network with insufficient measurements,the conception of virtual measurement for distribution network is presented based on matching power flow to simplify state estimation methods,improve computational accuracy and optimize distribution network state.The mathematical model for virtual measurements is built with the objective of adaptive weighted least square estimation of voltage measurement under the constraint conditions of power flow matching,and power mismatch allocation parameter equations.Then the particle swarm optimization method is given by introducing of chaos optimization and strong induction-enhanced mechanism to solve the state estimation question,and the application process of virtual measurement for distribution network is introduced in detail.The tests are carried out on IEEE8,IEEE33 and IEEE69 system respectively based on proposed method,and results show that the accuracy state of distribution network can be obtained through less iterating times without need of measurement transformation and matrix calculation,which has good application prospect.
Keywords:distribution network  virtual measurement  power flow matching technique  chaos  induction-enhanced mechanism  adaptive estimation  particle swarm optimization algorithm
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