基于PMU测量数据在线参数辨识的故障定位增广状态估计方法

Junjuan Li, Xiaojun Wang, Xinyu Ren, Yongjie Zhang, Fang Zhang
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引用次数: 5

摘要

当电网发生故障时,准确、及时的故障定位对减少故障损失具有重要意义。随着同步相量测量技术的迅速发展,同步相量测量所带来的高精度大数据为配电网的在线故障诊断带来了多种可能性。提出了一种基于在线参数辨识的增广状态估计故障定位方法。利用PMU测量的初始和终端电压、电流相量,推导出沿正序网络和负序网络电压、电流的关系。将线路参数和故障信息(故障距离和故障点电压相量)扩充到状态量中,与原始节点状态量一起进行状态估计,实现在线参数识别和准确故障定位。在PSCAD中建立了径向配电网模型。仿真结果验证了该算法的正确性和高精度。
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Augmented State Estimation Method for Fault Location Based on On-line Parameter Identification of PMU Measurement Data
When a fault occurs in power grid, accurate and timely fault location is of great significance in reducing fault losses. With the rapid development of synchronous phasor measurement technology, the high-precision big data brought by the synchronous phasor measurement brings many possibilities to the on-line fault diagnosis of the distribution network. This paper proposed an augmented state estimation fault location method based on on-line parameter identification. By using the initial and terminal voltage and current phasor measured by PMU, the relationship between voltage and current along the positive sequence network and negative sequence network is deduced. The line parameters and the fault information (the fault distance and the voltage phasor of the fault point) are augmented into the state quantity, and the state estimation is performed together with the original node state quantity to realize on-line parameter identification and accurate fault location. The radial distribution network model is built in PSCAD. The simulation results verify the correctness and high precision of the proposed algorithm.
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