Ensemble voting-based fault classification and location identification for a distribution system with microgrids using smart meter measurements

Islam, Md Maidul; Usman, Muhammad Usama; Newaz, Alvi; Faruque, Md Omar

doi:10.1049/stg2.12091

Ensemble voting-based fault classification and location identification for a distribution system with microgrids using smart meter measurements

Journal Article · Fri Oct 28 00:00:00 EDT 2022 · IET Smart Grid

DOI:https://doi.org/10.1049/stg2.12091· OSTI ID:1924023

^[1]; Usman, Muhammad Usama ^[2]; Newaz, Alvi ^[1]; Faruque, Md Omar ^[1]

Florida State Univ., Tallahassee, FL (United States)
Idaho National Lab. (INL), Idaho Falls, ID (United States)

This study presents an ensemble learning approach for fault classification and location identification in a smart distribution network containing photovoltaics (PV)-based microgrid. Lack of available data points and the unbalanced nature of the distribution system make fault handling a challenging task for utilities. The proposed method uses event-driven voltage data from smart meters to classify and locate faults. The ensemble voting classifier is composed of three base learners; random forest, k-nearest neighbours, and artificial neural network. The fault location (FL) task has been formulated as a classification problem where the fault type is classified in the first step and based on the fault type, the faulty bus is identified. The method is tested on IEEE-123 bus system modified with added PV-based microgrid along with dynamic loading conditions and varying fault resistances from 0 to 20 Ω for both unbalanced and balanced fault types. A further sensitivity analysis has been done to test the robustness of the proposed method under various noise levels and data loss errors in the smart meter measurements. The ensemble method shows improved performance and robustness compared to some previously proposed FL methods. Finally, the proposed method has been experimentally validated on a real-time simulation-based testbed using a state-of-the-art digital real-time simulator, industry standard DNP3 communication protocol and a cpu-based control centre running the FL algorithm.

View Accepted Manuscript (DOE)

Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 1924023

Report Number(s):: INL/JOU-22-69343-Rev000

Journal Information:: IET Smart Grid, Journal Name: IET Smart Grid Journal Issue: 3 Vol. 6; ISSN 2515-2947

Publisher:: Institution of Engineering and Technology (IET)Copyright Statement

Country of Publication:: United States

Language:: English

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