Recognition of Multiple PQ Issues using Modified EMD and Neural Network Classifier
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 signa...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Iran University of Science and Technology
2018-06-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
| Subjects: | |
| Online Access: | http://ijeee.iust.ac.ir/browse.php?a_code=A-10-2093-1&slc_lang=en&sid=1 |
| _version_ | 1819161881252724736 |
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| author | M. K. Saini R. K. Beniwal |
| author_facet | M. K. Saini R. K. Beniwal |
| author_sort | M. K. Saini |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T17:19:23Z |
| format | Article |
| id | doaj.art-ee9b1a7781554b0cbcd147c5afb49f44 |
| institution | Directory Open Access Journal |
| issn | 1735-2827 2383-3890 |
| language | English |
| last_indexed | 2024-12-22T17:19:23Z |
| publishDate | 2018-06-01 |
| publisher | Iran University of Science and Technology |
| record_format | Article |
| series | Iranian Journal of Electrical and Electronic Engineering |
| spelling | doaj.art-ee9b1a7781554b0cbcd147c5afb49f442022-12-21T18:18:53ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902018-06-01142188203Recognition of Multiple PQ Issues using Modified EMD and Neural Network ClassifierM. K. Saini0R. K. Beniwal1 DCR University of Science & Technology, Murthal (Sonipat), INDIA DCR University of Science & Technology, Murthal (Sonipat), INDIA 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.http://ijeee.iust.ac.ir/browse.php?a_code=A-10-2093-1&slc_lang=en&sid=1Empirical Mode Decomposition Neural Network Power Quality Wavelet Transform. |
| spellingShingle | M. K. Saini R. K. Beniwal Recognition of Multiple PQ Issues using Modified EMD and Neural Network Classifier Iranian Journal of Electrical and Electronic Engineering Empirical Mode Decomposition Neural Network Power Quality Wavelet Transform. |
| title | Recognition of Multiple PQ Issues using Modified EMD and Neural Network Classifier |
| title_full | Recognition of Multiple PQ Issues using Modified EMD and Neural Network Classifier |
| title_fullStr | Recognition of Multiple PQ Issues using Modified EMD and Neural Network Classifier |
| title_full_unstemmed | Recognition of Multiple PQ Issues using Modified EMD and Neural Network Classifier |
| title_short | Recognition of Multiple PQ Issues using Modified EMD and Neural Network Classifier |
| title_sort | recognition of multiple pq issues using modified emd and neural network classifier |
| topic | Empirical Mode Decomposition Neural Network Power Quality Wavelet Transform. |
| url | http://ijeee.iust.ac.ir/browse.php?a_code=A-10-2093-1&slc_lang=en&sid=1 |
| work_keys_str_mv | AT mksaini recognitionofmultiplepqissuesusingmodifiedemdandneuralnetworkclassifier AT rkbeniwal recognitionofmultiplepqissuesusingmodifiedemdandneuralnetworkclassifier |