Title: An efficient pattern analysis-based event detection and fault localisation model for reliable power transmission system

Authors: Arunkumar Patil; T. Ananthapadmanaba; A.D. Kulkarni; N. Mohan

Addresses: Department of Electrical and Electronics Engineering, National Institute of Engineering, Mysuru,Karnataka, India ' NIE Institute of Technology, Mysuru, Karnataka, India ' Department of Electrical and Electronics Engineering, National Institute of Engineering, Mysuru, Karnataka, India ' Department of Electrical and Electronics Engineering, National Institute of Engineering, Mysuru, Karnataka, India

Abstract: The exponential rise of power demands across socio-industrial applications has motivated academia-industries to develop optimal power quality assurance to meet the requirements. The use of synchrophasor information-based event detection has always been a potential solution to ensure controllability of the power systems; however assessing the gigantic real-time phasor information is often an intricate task. On the contrary, accurate and timely data (feature) extraction and event detection are a must for decision purposes. Recently, the robustness of the soft computing techniques, particularly data mining and pattern classification approaches have gained significant attention for large sale data analysis and classification. With this motivation, in this paper we have developed a multi-stage intelligent soft computing model for event detection and fault localisation using the pattern classification algorithms named, J48, naïve Bayes and K-NN. The developed algorithm is verified with IEEE test systems. The overall results affirm the suitability of K-NN-based event detection and fault localisation model for real time purposes.

Keywords: automatic event detection; fault localisation; PMU; synchrophasor data; pattern classification.

DOI: 10.1504/IJSGGC.2018.095168

International Journal of Smart Grid and Green Communications, 2018 Vol.1 No.4, pp.292 - 318

Received: 08 Mar 2018
Accepted: 22 Mar 2018
Published online: 01 Oct 2018 *

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