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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/7579
Title: Neuro-fuzzy technique for power transformer protection
Authors: Tripathy, Manoj
Maheshwari R.P.
Verma H.K.
Published in: Electric Power Components and Systems
Abstract: In the quest of more reliable power transformer protection, differential protection is considered the best scheme. In the proposed scheme, various operating conditions of transformers are distinguished by virtue of the signatures of differential current. As the conditions of internal fault and magnetizing inrush do have some of the signatures common among them, it is becoming increasingly important and difficult to distinguish between magnetizing inrush and fault conditions for differential relaying. In this direction, both feature-based, as well as pattern-based, approaches are used. In this article, a new approach, based on neuro-fuzzy technique, is presented for power transformer protection that ensures relay stability against external fault, magnetizing inrush, sympathetic inrush, and over-excitation conditions and its operation on internal faults. This approach is able to handle the "vague" information rather than only the "crisp" information. In the proposed method, fuzzy back-propagation neural network (FBPNN) is used as a core classifier to discriminate between magnetizing inrush and internal fault of a power transformer. An algorithm has been developed using an optimal number of neurons in the hidden layer as well as in the output layer. The effect of hidden layer neurons on the classification accuracy is analyzed. The algorithm makes use of voltage-to-frequency ratio and amplitude of differential current for detection of transformer operating conditions. The performance of BPNN, radial basis function neural network (RBFNN), and probabilistic neural network (PNN) are compared with the proposed fuzzy BPNN. Extensive simulation studies have been performed to demonstrate the efficiency of the proposed scheme using PSCAD/EMTDC and MATLAB.
Citation: Electric Power Components and Systems (2008), 36(3): 299-316
URI: https://doi.org/10.1080/15325000701603967
http://repository.iitr.ac.in/handle/123456789/7579
Issue Date: 2008
Keywords: Artificial neural network
Digital differential power transformer protection
Fuzzy back-propagation neural network (FBPNN)
Fuzzy logic
Protective relaying
ISSN: 15325008
Author Scopus IDs: 16205441100
8941720600
57204684351
Author Affiliations: Tripathy, M., Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Roorkee, Uttarakhand, India
Maheshwari, R.P., Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Roorkee, Uttarakhand, India, Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Roorkee, Uttarakhand, 247 667, India
Verma, H.K., Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Roorkee, Uttarakhand, India
Corresponding Author: Maheshwari, R.P.; Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Roorkee, Uttarakhand, 247 667, India; email: rudrafee@iitr
Appears in Collections:Journal Publications [EE]

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