Detection and Localization of Transmission Line Faults based on a Hybrid Two-Stage Technique considering Wind Power Generation

Conference paper (2021)

Authors

Shahabodin Afrasiabi Shiraz University
Mousa Afrasiabi Shiraz University
B. Behdani Shiraz University
Mohammad Mohammadi Shiraz University
Mohammad S. Javadi INESC TEC (Formerly INESC Porto)
Gerardo J. Osório C-MAST/UBI, REMIT/UPT
João P.S. Catalão INESC-Tec, Universidade do Porto
Affiliation
External organisation
Transmission System Fault Detection and Location Robust Soft Learning Vector Quantization (RSLVQ) T-Distributed Stochastic Neighbor Embedding (t-SNE)
More Info
expand_more

Published Date

2021

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Affiliation

External organisation

Abstract

The conflicting issues of growing demand for electrical energy versus the environmental concerns have left the energy industries practically with one choice: to turn into renewable energies. This duality has also highlighted the role of power transmission systems as energy delivery links in two ways, considering the increased demand of load centers, and the integration of large-scale renewable generation units connected to the transmission system such as wind power generation. Accordingly, it has become even more vital to provide reliable protection for the power transmission links. The present protection methods are associated with deficiencies e.g., acting based on a predefined threshold, low speed, and the requirement of costly devices. A two-stage data-driven-based methodology has been introduced in this paper to deal with such defects, considering wind power generation. The proposed approach utilizes a powerful feature extraction technique, namely the t-distributed stochastic neighbor embedding (t-SNE) in the first stage. In the second stage, the extracted features are fed to a robust soft learning vector quantization (RSLVQ) classifier to detect and locate transmission line faults. The WSCC 9-bus system is used to evaluate the performance of the proposed data-driven method during various system operating conditions. The obtained results verify the promising capability of the proposed approach in detecting and locating transmission line faults.