A novel data selection technique using fuzzy C-means clustering to enhance SVM-based power quality classification
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| 001 | 605470898 | ||
| 003 | CHVBK | ||
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| 008 | 210128e20151101xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00500-014-1472-9 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00500-014-1472-9 | ||
| 245 | 0 | 2 | |a A novel data selection technique using fuzzy C-means clustering to enhance SVM-based power quality classification |h [Elektronische Daten] |c [K. Manimala, Indra David, K. Selvi] |
| 520 | 3 | |a In this paper, a novel data selection algorithm for identifying significant training data is presented for power quality (PQ) events classification. The key concept of this paper is to reduce the execution time, computational complexity and enhance the accuracy of the existing PQ classification system by reducing the number of support vectors. The proposed scheme identifies important training data and rejects redundant and irrelevant ones using a novel fuzzy C-means clustering-based data selection algorithm thereby reducing time of classification and enhancing accuracy. Significant features from raw PQ data are extracted using discrete wavelet transform and important training data are recognized using these features with the proposed data selection algorithm. Two machine learning algorithms namely the probabilistic neural network and support vector machine are employed and the best PQ classifier is investigated. Furthermore, the proposed data selection algorithm is integrated with the existing PQ classification system that has selected optimal input features and parameters of the classifier using Simulated Annealing and is shown to perform exceptionally well when compared to conventional classifiers that use full training data set. The suitability of the algorithm for noisy as well as real time data is examined and the empirical results show that the proposed scheme performs well compared to several existing PQ classification works. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a Support vector machine |2 nationallicence | |
| 690 | 7 | |a Probabilistic neural network |2 nationallicence | |
| 690 | 7 | |a Power quality |2 nationallicence | |
| 690 | 7 | |a Wavelet transform |2 nationallicence | |
| 690 | 7 | |a Fuzzy C-means clustering |2 nationallicence | |
| 700 | 1 | |a Manimala |D K. |u Department of ECE, Dr. Sivanthi Aditanar College of Engineering, 628 215, Tiruchendur, Tamilnadu, India |4 aut | |
| 700 | 1 | |a David |D Indra |u Department of EEE, Government College of Engineering, 627 007, Tirunelveli, Tamilnadu, India |4 aut | |
| 700 | 1 | |a Selvi |D K. |u Department of EEE, Thiagarajar College of Engineering, 625 015, Madurai, Tamilnadu, India |4 aut | |
| 773 | 0 | |t Soft Computing |d Springer Berlin Heidelberg |g 19/11(2015-11-01), 3123-3144 |x 1432-7643 |q 19:11<3123 |1 2015 |2 19 |o 500 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00500-014-1472-9 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00500-014-1472-9 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Manimala |D K. |u Department of ECE, Dr. Sivanthi Aditanar College of Engineering, 628 215, Tiruchendur, Tamilnadu, India |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a David |D Indra |u Department of EEE, Government College of Engineering, 627 007, Tirunelveli, Tamilnadu, India |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Selvi |D K. |u Department of EEE, Thiagarajar College of Engineering, 625 015, Madurai, Tamilnadu, India |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Soft Computing |d Springer Berlin Heidelberg |g 19/11(2015-11-01), 3123-3144 |x 1432-7643 |q 19:11<3123 |1 2015 |2 19 |o 500 | ||