Dual State - Parameter Estimation for Series Arc Fault Detection on a DC Microgrid
Dual State - Parameter Estimation for Series Arc Fault Detection on a DC Microgrid
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DOI：
10.1109/ecce44975.2020.9235753
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
K. Gajula;Xiu Yao;L. Herrera
中科院分区：
文献类型：
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作者：
K. Gajula;Xiu Yao;L. Herrera
本文提出了一种基于双状态和双参数估计（SE和PE）的串联直流电弧故障检测和定位方法。直流微电网故障电流小，因此其串联电弧故障检测具有挑战性。通过使用在一段时间内的传感器测量数据的可用集合，基于最小二乘（LS）的SE算法估计直流微电网的总线电压和注入电流。卡尔曼滤波器（KF），然后用于估计网络中的线路电导，这是用来检测和定位（相对于故障线路）的串联电弧故障。仿真结果与不同的案例研究，以证明该算法的鲁棒性正常的操作条件下，不同的数量和放置的传感器。最后，控制硬件在环（CHIL）的结果显示。
In this paper, a detection and localization technique based on dual State and Parameter Estimation (SE and PE respectively) for series dc arc faults is presented. Detection of series arc faults in dc microgrids is challenging due to its low fault current. By using the available set of sensor measurement data over a period of time, a Least Squares (LS) based SE algorithm estimates the dc microgrid’s bus voltages and injection currents. Kalman Filter (KF) is then used to estimate the line conductances in the network, which are used to detect and localize (with respect to the faulted line) the series arc fault. Simulation results are presented with different case studies to demonstrate the robustness of the algorithm to normal operating conditions and different number and placement of sensors. Finally, Control Hardware in the Loop (CHIL) results are shown.