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

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
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
Recognition of Multiple PQ Issues using Modified EMD and Neural Network Classifier
M. K. Saini , R. K. Beniwal
Abstract:   (4316 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.
Keywords: Empirical Mode Decomposition , Neural Network , Power Quality , Wavelet Transform
Full-Text [PDF 1965 kb]   (1722 Downloads)    
Type of Study: Research Paper | Subject: Power Quality
Received: 2017/04/01 | Revised: 2018/06/17 | Accepted: 2017/12/26
Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 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.