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Paper Title
Highly Reliable Transmission Line Fault Detection and Classification Technique Using Hybrid Wavelet-PCA Feature and KNN Classifier
  Manish Kurre,  Shailesh.M.Deshmukh

In the present era, the power generation has become a vital part to provide continuous supply of power to the consumers. The efficiency of a power system depends on how a fault is accurately detected and classified, so that quick restoration and maintenance of power is accomplished. Fault detection, fault classification, needs to be performed using a fast and responsive algorithm at different levels of a power system. The significant factors which affect the operation of power line during the occurrence of faults are mainly; fault impedance, fault inception angle (FIA), and fault distance. These factors can be analyzed to detect the occurrence of the disturbance in the power line operation. Various techniques like Fourier transform, short time Fourier transform (STFT) and wavelet transform have been used in past to detect and classify the different faults occurred in the transmission line. However these conventional features based techniques especially wavelet transform based technique provides good fault detection and classification accuracy but highly suffers from the influence of system parameters. The main limitation of the conventional techniques is the selection of system dependent threshold value for the detection and classification of faults. The proper selection of threshold value is a very tedious and time consuming task and also requires brief knowledge of the system configuration. To avoid the drawbacks of conventional fault detection and classification techniques, this paper proposed, an efficient and robust fault detection and classification technique using hybrid wavelet-PCA feature along with efficient K- Nearest Neighbor (KNN) classifier. The advantage of the proposed technique is that; it doesn’t require any threshold selection for the detection and classification of faults. The fault detection and classification accuracy of proposed technique has been verified using MATLAB/Simulink 2013(a) software. The obtained results shows that the proposed technique is efficient in detection and classification of all type of faults and hence reliable tool for detection and classification of faults occurred in transmission line.

Keywords- Transmission line, fault detection and classification, wavelet feature, multi-resolution analysis.
Publication Details
Unique Identification Number - IJEDR1602236
Page Number(s) - 1329-1336
Pubished in - Volume 4 | Issue 2 | May 2016
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
  Manish Kurre,  Shailesh.M.Deshmukh,   "Highly Reliable Transmission Line Fault Detection and Classification Technique Using Hybrid Wavelet-PCA Feature and KNN Classifier", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.4, Issue 2, pp.1329-1336, May 2016, Available at :http://www.ijedr.org/papers/IJEDR1602236.pdf
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