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多采样周期混合量测环境下的主动配电网状态估计方法
引用本文:王少芳,刘广一,黄仁乐,秦帅.多采样周期混合量测环境下的主动配电网状态估计方法[J].电力系统自动化,2016,40(19):30-36.
作者姓名:王少芳  刘广一  黄仁乐  秦帅
作者单位:中国电力科学研究院, 北京市 100192,全球能源互联网研究院, 北京市 102209,国网北京市电力公司, 北京市 100031,北京电力经济技术研究院, 北京市 100055
基金项目:国家高技术研究发展计划(863计划)资助项目(2014AA051901);国家自然科学基金资助项目(51261130472);国家电网公司科技项目“面向分布式电源接入的主配网一体化分析及快速仿真关键技术研究”
摘    要:针对主动配电网中远程终端单元(RTU)、相量测量单元(PMU)与高级量测体系(AMI)多采样周期量测数据长期共存的实际情况,提出了一种基于RTU,PMU,AMI混合量测的主动配电网状态估计混合算法。该混合算法由非线性静态状态估计、线性静态状态估计与线性动态状态估计3种算法组成。线性动态状态估计与线性静态状态估计利用PMU量测与RTU量测,实时跟踪系统注入节点有功功率与无功功率的变化,在非AMI量测的采集时刻,为非线性静态状态估计提供高精度的虚拟量测。所提算法缩短了非线性静态状态估计的计算周期,提高了非线性静态状态估计的精度,提升了对主动配电网运行状态的预测能力。通过算例仿真,验证了所提算法的有效性。

关 键 词:主动配电网  静态状态估计  动态状态估计  混合量测  多源量测
收稿时间:2016/3/18 0:00:00
修稿时间:7/6/2016 12:00:00 AM

State Estimation Method for Active Distribution Networks Under Environment of Hybrid Measurements with Multiple Sampling Periods
WANG Shaofang,LIU Guangyi,HUANG Renle and QIN Shuai.State Estimation Method for Active Distribution Networks Under Environment of Hybrid Measurements with Multiple Sampling Periods[J].Automation of Electric Power Systems,2016,40(19):30-36.
Authors:WANG Shaofang  LIU Guangyi  HUANG Renle and QIN Shuai
Affiliation:China Electric Power Research Institute, Beijing 100192, China,Global Energy Interconnection Research Institute, Beijing 102209, China,State Grid Beijing Electric Power Company, Beijing 100031, China and Beijing Power Economic Research Institute, Beijing 100055, China
Abstract:According to the fact of long-term coexistence of the remote terminal unit(RTU), the phasor measurement unit(PMU)and the advanced metering infrastructure(AMI)in active distribution networks, this paper presents a hybrid algorithm of state estimation based on hybrid measurements of RTU, PMU and AMI, which consists of nonlinear static state estimation, linear static state estimation and linear dynamic state estimation. Linear dynamic state estimation and linear static state estimation track the changes in node injected active and reactive power by utilizing PMU and RTU measurements, which can provide highly accurate virtual measurements for nonlinear static state estimation in the absence of AMI measurements. The proposed algorithm can shorten the calculation cycle and improve the accuracy of nonlinear static state estimation, while enhancing the ability of state forecasting for active distribution networks. The effectiveness of the proposed algorithm is verified by a numerical simulation. This work is supported by National High Technology Research and Development Program of China(863 Program)(No. 2014AA051901), National Natural Science Foundation of China(No. 51261130472)and State Grid Corporation of China.
Keywords:active distribution network(ADN)  static state estimation  dynamic state estimation  hybrid measurement  multi-source measurement
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