Combined Approach of PNN and Time-Frequency as the Classifier for Power System Transient Problems

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ID: 32375
2013
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Abstract
The transients in power system cause serious disturbances in the reliability, safety and economy of the system. The transient signals possess the nonstationary characteristics in which the frequency as well as varying time information is compulsory for the analysis. Hence, it is vital, first to detect and classify the type of transient fault and then to mitigate them. This article proposes time-frequency and FFNN (Feedforward Neural Network) approach for the classification of power system transients problems. In this work it is suggested that all the major categories of transients are simulated, de-noised, and decomposed with DWT (Discrete Wavelet) and MRA (Multiresolution Analysis) algorithm and then distinctive features are extracted to get optimal vector as input for training of PNN (Probabilistic Neural Network) classifier. The simulation results of proposed approach prove their simplicity, accurateness and effectiveness for the automatic detection and classification of PST (Power System Transient) types
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memon2013combinedmehran Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Memon, Aslam Pervez;Uqaili, Muhammad Aslam;Memon, Zubair Ahmed;
Journal mehran university research journal of engineering and technology
Year 2013
DOI
DOI not found
URL
http://publications.muet.edu.pk/research_papers/pdf/pdf816.pdf
Keywords Keywords not found

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