Application of extreme learning machine for series compensated transmission line protection


Malathi, V. and Marimuthu, N.S. and Baskar, S. and Ramar, K. (2011) Application of extreme learning machine for series compensated transmission line protection. Engineering Applications of Artificial Intelligence, 24 (5). pp. 880-887. ISSN 09521976

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This paper proposes a new approach based on combined Wavelet Transform-Extreme Learning Machine (WT-ELM) technique for fault section identification (whether the fault is before or after the series capacitor as observed from the relay point), classification and location in a series compensated transmission line. This method uses the samples of fault currents for half cycle duration from the inception of fault. The features of fault currents are extracted by first level decomposition of the current samples using discrete wavelet transform (DWT) and the extracted features are applied as inputs to ELMs for fault section identification, classification and location. The feasibility of the proposed method has been tested on a 400 kV, 300 km series compensated transmission line for all the ten types of faults using MATLAB simulink. On testing 28,800 fault cases with varying fault resistance, fault inception angle, fault distance, load angle, percentage compensation level and source impedance, the performance of the proposed method has been found to be quite promising. The results also indicate that the proposed method is robust to wide variation in system and operating conditions. (c) 2011 Elsevier Ltd. All rights reserved.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering (FOE)
Depositing User: Users 27 not found.
Date Deposited: 08 Aug 2011 06:14
Last Modified: 08 Aug 2011 06:14


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