Performance analysis of a novel AI based approach to fault classification and location in an active distribution network with type 3 and type 4 wind turbine connections

K. Lout, R.K. Aggarwal

Research output: Contribution to conferencePaper

Abstract

Accurate fault location in an electrical power system improves the response time to short circuit faults on the system and increases the system reliability. This paper presents novel Artificial Neural Network algorithms that identify with high accuracy whether a short circuit fault lies on a feeder or on one of the spurs. These algorithms are also able to evaluate the distance to the point of fault on the feeder or a spur using only the phase currents measured at the substation. Further tests demonstrate the robustness of the proposed method to the integration of doubly fed induction generator and permanent magnet synchronous generator wind turbines into the network.
Original languageEnglish
Pages4.1.1
Number of pages6
DOIs
Publication statusPublished - 2014
Event12th IET International Conference on Developments in Power System Protection, DPSP 2014 - Copenhagen , Denmark
Duration: 31 Mar 20143 Apr 2014

Conference

Conference12th IET International Conference on Developments in Power System Protection, DPSP 2014
CountryDenmark
CityCopenhagen
Period31/03/143/04/14

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    Lout, K., & Aggarwal, R. K. (2014). Performance analysis of a novel AI based approach to fault classification and location in an active distribution network with type 3 and type 4 wind turbine connections. 4.1.1. Paper presented at 12th IET International Conference on Developments in Power System Protection, DPSP 2014, Copenhagen , Denmark. https://doi.org/10.1049/cp.2014.0021