Fault Detection in Distribution Network with the Cauchy-M Estimate—RVFLN Method

Cem Haydaroğlu () and Bilal Gümüş
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Cem Haydaroğlu: Electrical and Electronics Engineering Department, Faculty of Engineering, Dicle University, Diyarbakır 21280, Turkey
Bilal Gümüş: Electrical and Electronics Engineering Department, Faculty of Engineering, Dicle University, Diyarbakır 21280, Turkey

Energies, 2022, vol. 16, issue 1, 1-18

Abstract: Fault detection is an important issue in today’s distribution networks, the structure of which is becoming more complex. In this article, a data-based Cauchy distribution weighting M-estimate RVFLNs method is proposed for short-circuit fault detection in distribution networks. The proposed method detects short circuits based on current and voltage measurements. In addition, noises were added to the data to ensure the robustness of the method. The performance of the method was examined in the RTDS RTS simulator using the IEEE 33-bus-bar system model with the help of real-time simulations. The success rate of the proposed method is between 98% and 100% for low-impedance (0 ohm) short-circuit faults, depending on the fault type. The success rate of high-impedance (100 ohm) short-circuit faults, which are more difficult to detect, is between 80% and 92%, depending on the fault type.

Keywords: RVFLN; robust RVFLN; Cauchy-M estimate; IEEE 33-bus model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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