Programs
- M. Tech. in Automotive Engineering -
- Clinical Fellowship in Laboratory Genetics & Genomics - Fellowship
Publication Type : Journal Article
Publisher : IEEE
Source : IEEE Systems Journal
Url : https://ieeexplore.ieee.org/abstract/document/8611108
Campus : Bengaluru
School : School of Artificial Intelligence
Year : 2019
Abstract : This paper proposes a new method for diagnosis of fault type and faulty phase of a series compensated transmission line. The standard deviation (SD) principle together with the fast discrete orthonormal s-transform (FDOST) and the decision tree (DT) is applied for the purpose of fault classification. The FDOST, as an efficient signal processing tool, is used for extracting the features from a half cycle window of voltage and current signals sampled from one end of the power system network. Finally, the SD of a half cycle post-fault samples of the FDOST coefficients is calculated to form the input feature vector for the DT-based classifier. The features are processed by the DT to classify faults. The practicability of the proposed method is validated by modified Western System Coordinating Council 3-machine 9-bus system simulated in the PSCAD/EMTDC software and field fault data captured from a real transmission network of Chhattisgarh state, India. The results confirm that the proposed method reliably classifies all types of faults with high efficacy.
Cite this Research Publication : Praveen Kumar Mishra, Anamika Yadav, M. Pazoki, FDOST-Based Fault Classification Scheme for Fixed Series Compensated Transmission System, IEEE Systems Journal, 2019.