Application of slantlet transform based support vector machine for power quality detection and classification
Concern towards power quality (PQ) has increased immensely due to the growing usage of high technology devices which are very sensitive towards voltage and current variations and the de-regulation of the electricity market. The impact of these voltage and current variations can lead to devices malfu...
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| Format: | Article |
| Language: | English |
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Scientific Research Publishing
2015
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| Online Access: | http://eprints.uthm.edu.my/5660/1/AJ%202015%20%2814%29.pdf |
| _version_ | 1796869236788822016 |
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| author | Mohd Noh, Faridah Hanim Miyauchi, Hajime Yaakub, M. Faizal |
| author_facet | Mohd Noh, Faridah Hanim Miyauchi, Hajime Yaakub, M. Faizal |
| author_sort | Mohd Noh, Faridah Hanim |
| collection | UTHM |
| description | Concern towards power quality (PQ) has increased immensely due to the growing usage of high technology devices which are very sensitive towards voltage and current variations and the de-regulation of the electricity market. The impact of these voltage and current variations can lead to devices malfunction and production stoppages which lead to huge financial loss for the produc-tion company. The deregulation of electricity markets has made the industry become more com-petitive and distributed. Thus, a higher demand on reliability and quality of services will be re-quired by the end customers. To ensure the power supply is at the highest quality, an automatic system for detection and localization of PQ activities in power system network is required. This paper proposed to use Slantlet Transform (SLT) with Support Vector Machine (SVM) to detect and localize several PQ disturbance, i.e. voltage sag, voltage swell, oscillatory-transient, odd-harmonics, interruption, voltage sag plus odd-harmonics, voltage swell plus odd-harmonics, voltage sag plus transient and pure sinewave signal were studied. The analysis on PQ disturbances signals was performed in two steps, which are extraction of feature disturbance and classification of the dis- turbance based on its type. To take on the characteristics of PQ signals, feature vector was con-structed from the statistical value of the SLT signal coefficient and wavelets entropy at different nodes. The feature vectors of the PQ disturbances are then applied to SVM for the classification process. The result shows that the proposed method can detect and localize different type of single and multiple power quality signals. Finally, sensitivity of the proposed algorithm under noisy con-dition is investigated in this paper. |
| first_indexed | 2024-03-05T21:51:51Z |
| format | Article |
| id | uthm.eprints-5660 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:51:51Z |
| publishDate | 2015 |
| publisher | Scientific Research Publishing |
| record_format | dspace |
| spelling | uthm.eprints-56602022-01-20T02:14:36Z http://eprints.uthm.edu.my/5660/ Application of slantlet transform based support vector machine for power quality detection and classification Mohd Noh, Faridah Hanim Miyauchi, Hajime Yaakub, M. Faizal TJ241-254.7 Machine construction (General) Concern towards power quality (PQ) has increased immensely due to the growing usage of high technology devices which are very sensitive towards voltage and current variations and the de-regulation of the electricity market. The impact of these voltage and current variations can lead to devices malfunction and production stoppages which lead to huge financial loss for the produc-tion company. The deregulation of electricity markets has made the industry become more com-petitive and distributed. Thus, a higher demand on reliability and quality of services will be re-quired by the end customers. To ensure the power supply is at the highest quality, an automatic system for detection and localization of PQ activities in power system network is required. This paper proposed to use Slantlet Transform (SLT) with Support Vector Machine (SVM) to detect and localize several PQ disturbance, i.e. voltage sag, voltage swell, oscillatory-transient, odd-harmonics, interruption, voltage sag plus odd-harmonics, voltage swell plus odd-harmonics, voltage sag plus transient and pure sinewave signal were studied. The analysis on PQ disturbances signals was performed in two steps, which are extraction of feature disturbance and classification of the dis- turbance based on its type. To take on the characteristics of PQ signals, feature vector was con-structed from the statistical value of the SLT signal coefficient and wavelets entropy at different nodes. The feature vectors of the PQ disturbances are then applied to SVM for the classification process. The result shows that the proposed method can detect and localize different type of single and multiple power quality signals. Finally, sensitivity of the proposed algorithm under noisy con-dition is investigated in this paper. Scientific Research Publishing 2015 Article PeerReviewed text en http://eprints.uthm.edu.my/5660/1/AJ%202015%20%2814%29.pdf Mohd Noh, Faridah Hanim and Miyauchi, Hajime and Yaakub, M. Faizal (2015) Application of slantlet transform based support vector machine for power quality detection and classification. Journal of Power and Energy Engineering,, 4 (215). pp. 215-223. ISSN 2327-5901 https://doi.org/10.4236/jpee.2015.34030 |
| spellingShingle | TJ241-254.7 Machine construction (General) Mohd Noh, Faridah Hanim Miyauchi, Hajime Yaakub, M. Faizal Application of slantlet transform based support vector machine for power quality detection and classification |
| title | Application of slantlet transform based support vector machine for power quality detection and classification |
| title_full | Application of slantlet transform based support vector machine for power quality detection and classification |
| title_fullStr | Application of slantlet transform based support vector machine for power quality detection and classification |
| title_full_unstemmed | Application of slantlet transform based support vector machine for power quality detection and classification |
| title_short | Application of slantlet transform based support vector machine for power quality detection and classification |
| title_sort | application of slantlet transform based support vector machine for power quality detection and classification |
| topic | TJ241-254.7 Machine construction (General) |
| url | http://eprints.uthm.edu.my/5660/1/AJ%202015%20%2814%29.pdf |
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