A robust state estimator for medium voltage distribution networks
A robust state estimator for medium voltage distribution networks
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DOI：
10.1109/tpwrs.2012.2215927
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发表时间：
2013-05
影响因子：
6.6
通讯作者：
Jianzhong Wu;Yan He;N. Jenkins
中科院分区：
文献类型：
--
作者：
Jianzhong Wu;Yan He;N. Jenkins
研究了一种闭环鲁棒分布状态估计器。提出了一种适用于中压配电网的方法，该方法既适用于实时测量值有限的欠确定情况，也适用于智能电表信息延迟的过确定情况。该状态估计器具有较强的鲁棒性，可以抵抗测量误差、测量类型、位置和精度以及智能计量通信系统临时故障的影响。机器学习函数为鲁棒状态估计算法提供可靠的输入信息。然后将状态估计器的输出反馈给机器学习函数，创建闭环信息流，从而提高状态估计器的性能。给出了一个33节点系统的测试结果和分析。
A closed-loop robust distribution state estimator was investigated. An approach that is suitable for medium voltage distribution networks which are either under-determined with limited real-time measurements or over-determined but with delayed information from smart meters was developed. The state estimator was designed to be robust against the effect of measurement errors, the type, location and accuracy of measurements, as well as temporary failure of the smart metering communication system. A machine learning function provides reliable input information to a robust state estimation algorithm. The output of the state estimator is then fed back to the machine learning function creating a closed-loop information flow which improves the performance of the state estimator. Test results and analysis on a 33-node system are provided.