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文章摘要
配电系统关键量测解耦快速辨识及鲁棒量测优化配置
Fast Decoupled Identification of Critical Measurement and Robust Measurement Optimal Placement for Distribution System
Received:September 18, 2016  Revised:September 18, 2016
DOI:
中文关键词: 配电系统  状态估计  关键量测解耦辨识  鲁棒量测优化配置
英文关键词: distribution  system, state  estimation, decoupled  identification of  critical measurement, robust  measurement optimal  placement
基金项目:
Author NameAffiliationE-mail
Qi Yan Tianjin Electric Power Research Institute qiyan_fly@163.com 
Liang Dong* Key Laboratory of Smart Grid of Ministry of Education liangdong@tju.edu.cn 
Dong Yichao Key Laboratory of Smart Grid of Ministry of Education ycdong007@tju.edu.cn 
Wang Shouxiang Key Laboratory of Smart Grid of Ministry of Education sxwang@tju.edu.cn 
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中文摘要:
      对配电系统实时运行状态的准确感知离不开状态估计,状态估计的正常运行与量测系统状况紧密相关。本文提出了一种针对三相不平衡配电系统的关键量测和关键量测组解耦快速辨识的新方法,该方法实现了关键量测和关键量测组辨识的解耦和非迭代:一方面,辨识过程仅需进行有功功率部分的状态估计,极大降低了计算规模;另一方面,整个计算过程无需迭代,并给出了相应的严格理论证明。此外,本文建立了配电系统三相m-1鲁棒量测优化配置模型,以保证任一量测缺失时网络仍然可观测。将所提方法和模型应用于改进的IEEE 37节点算例进行测试,计算结果显示关键量测解耦快速辨识方法的辨识速度远快于解耦前,鲁棒量测优化配置显著降低了配电系统不可观测的风险。
英文摘要:
      The accurate perception of distribution system depends on the state estimation and the successful operation of state estimation depends on the measurement system. This paper proposes a novel method concerning fast decoupled identification of critical measurement. The method can identify all critical measurements and critical measurement sets in a decoupled and non-iterative manner. On one hand, only state estimation calculation relevant to active power measurements is needed, which greatly reduces the computational scale. On the other hand, no iteration is needed and relevant strict theoretical proof is given. Besides, a three-phase m-1 robust measurement placement model is proposed, aiming to guarantee the network observability in the case of any measurement being lost. Test results based on the modified IEEE 37 node test feeder show that the decoupled method attains much faster speed than coupled one and the robust measurement optimal placement significantly reduces the risk of unobservability.
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