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DC networks are becoming more popular in a wide range of applications. However, the difficulty in detecting and localizing a high impedance series arc fault presents, a major challenge slowing the wider deployment of dc networks/microgrids. In this paper, a Kalman Filter (KF) based algorithm to monitor the operation of a dc microgrid by estimating the line admittances and consequently detecting/localizing series arc faults is introduced. The proposed algorithm uses voltage and current samples from the nodes in the distribution network to estimate the line admittances. By determining these values, it is possible to quickly isolate the faulted section and reconfigure the network after a fault occurs. Since, the disturbance caused by a high impedance series arc fault spreads across almost the entire microgrid, the KF algorithm is structured to detect the faulted line in the grid with precision. Simulation and Control Hardware in the Loop (CHIL) results are presented demonstrating the feasibility of implementation.
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Dual State - Parameter Estimation for Series Arc Fault Detection on a DC Microgrid
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.
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- PAR ID:
- 10292878
- Date Published:
- Journal Name:
- 2020 IEEE Energy Conversion Congress and Exposition (ECCE)
- Page Range / eLocation ID:
- 4649 to 4655
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ECCE44975.2020.9235753
