Volume 14, Issue 2 (June 2018)                   IJEEE 2018, 14(2): 188-203 | Back to browse issues page

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Saini M K, Beniwal R K. Recognition of Multiple PQ Issues using Modified EMD and Neural Network Classifier. IJEEE. 2018; 14 (2) :188-203
URL: http://ijeee.iust.ac.ir/article-1-1074-en.html
Abstract:   (1551 Views)
This paper presents a new framework based on modified EMD method for detection of single and multiple PQ issues. In modified EMD, DWT precedes traditional EMD process. This scheme makes EMD better by eliminating the mode mixing problem. This is a two step algorithm; in the first step, input PQ signal is decomposed in low and high frequency components using DWT. In the second stage, the low frequency component is further processed with EMD technique to get IMFs. Eight features are extracted from IMFs of low frequency component. Unlike low frequency component, features are directly extracted from the high frequency component. All these features form feature vector which is fed to PNN classifier for classification of PQ issues. For comparative analysis of performance of PNN, results are compared with SVM classifier. Moreover, performance of proposed methodology is also validated with noisy PQ signals. PNN has outperformed SVM for both noiseless and noisy PQ signals.
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Type of Study: Research Paper | Subject: Power Quality
Received: 2017/04/01 | Accepted: 2017/12/26 | Published: 2017/12/29

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© 2019 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.