Locating high-impedance faults in DC microgrid clusters using support vector machines

Navid Bayati, Ebrahim Balouji, Hamid Reza Baghaee, Amin Hajizadeh, Mohsen Soltani, Zhengyu Lin and Mehdi Savaghebi

Applied Energy, 2022, vol. 308, issue C, No S0306261921015889

Abstract: With the increasing number of DC microgrids, DC microgrid clusters are emerging as a cost-effective solution. Therefore, due to the possible long distances between DC microgrids, once a fault occurs and is cleared, it should be located. Especially, locating high impedance faults (HIFs) is challenging. With communication-free fault locating methods, implementation costs can be reduced, and noise and delay of communication can be eliminated. In this paper, a novel localized fault location method using support vector machines (SVMs) is proposed for DC microgrid clusters. The purpose of this study is to facilitate the post fault conditions by locating the accurate place of the faults, even the challenging HIFs, by using the local measurements at one end of each line. The proposed scheme applies the faults, and fault features generated experimentally to the SVM, which is trained in Python for determining the fault location. The experimental test results prove that the proposed scheme is immune against disturbances, such as noise and bad calibration, and can efficiently and reliably estimate the location and resistance of faults with high accuracy.

Keywords: DC Microgrid; SVM; Fault; Clusters (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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DOI: 10.1016/j.apenergy.2021.118338

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