S-transform-based classification of power quality disturbance signals by support vector machines

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering

Volumn 25, Issue 4, 2005, Pages 51-56

  Zhan, Yong ^a   Cheng, Hao Zhong ^a   Ding, Yi Feng ^a   Lu, Gan Yun ^a   Sun, Yi Bin ^b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b Shanghai Urban Power Supply Company   (China)

Author keywords

Disturbance classification; Power quality; Power system; S transform; Support Vector Machines

Indexed keywords

COMPUTER SIMULATION; FEATURE EXTRACTION; FOURIER TRANSFORMS; NUMERICAL ANALYSIS; PATTERN RECOGNITION; QUALITY CONTROL; WAVELET TRANSFORMS;

DISTURBANCE CLASSIFICATION; ELECTRIC POWER QUALITY DISTURBANCES; NUMERICAL SIMULATION; POWER QUALITY; SUPPORT VECTOR MACHINES; TIME FREQUENCY CHARACTERISTICS;

ELECTRIC POWER SYSTEMS;

EID: 16644397169     PISSN: 02588013     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Article

References (17)

1. Chilukuri M V, Dash P K. Multiresolution S-transform-based fuzzy recognition system for power quality events[J]. IEEE Tran. On Power Delivery, 2004, 19(1): 323-330.
2. Zhang Fusheng, Geng Zhongxing, Ge Yaozhong. FFT algorithm with high accuracy for harmonic analysis in power system[J]. Proceedings of the CSEE, 1999, 19(3): 63-66.
3. Chen Xiangxun. Wavelet-based measurements and classification of short duration power quality disturbances[J]. Proceedings of the CSEE, 2002, 22(10): 1-6.
4. Xu Yonghai, Xiao Xiangning, Yang Yihan et al. Power quality disturbance identification using dq conversion-based neural classifier[J]. Automation of electric power system, 2001, 25(14): 24-28.
5. Abde-Gali T K, El-Saadany E F, Youssef A M et al. On-line disturbance recognition utilizing vector based fast match[C]. Pro. IEEE Power Eng. Soc. Summer Meeting, Chicago, IL, July 21-25, 2002.
6. Youssef A M, Abde-Gali T K, El-Saadany E F et al. Disturbance classification utilizing dynamic time warping classifier[J]. IEEE Trans. on Power Delivery, 2004, 19(1): 272-278.
7. Chung J, Powers E J, Grady W M et al. Power disturbance classifier utilizing a ruled-based method and wavelet packet-based hidden Markov model[J]. IEEE Trans. on Power Delivery, 2002, 17(1): 738-743.
8. Santoso S, Powers E J, Grady W M. Power quality disturbance identification using wavelet transforms and artificial neural networks[C]. Proceedings of IEEE ICHQP VII Lasvegas: 1996, 615-618.
9. Chung J, Powers E J, Grady W M et al. Power disturbance classifier using a rule based method and wavelet packet-based hidden Markov model[J]. IEEE Tran. Power Delivery, 2002, 17(1): 233-241.
10. Kezunovic M, Liao Y. A novel software implementation concept for power quality study[J]. IEEE Tran. on Power Delivery, 2002, 17(2): 998-1001.
11. Stockwell R G, Mansinha L, Lowe R P. Localization of the complex spectrum: The S transform[J]. IEEE Trans. on Signal Process, 1996, 44: 998-1001.
12. Lee I W C, Dash P K. S-transform-based intelligent system for classification of power quality disturbance signals[J]. IEEE Tran. on Power Delivery, 2003, 18(3): 800-805.
13. Vapnik V N. The nature of statistical learning theory[M]. Springer, 1995.
14. Li Yuancheng, Fang Tingjian, Yu Erkeng. Study of Support Vector Machines for short-term load forecasting[J]. Proceedings of the CSEE, 2003, 23(6): 55-59.
15. Dong Ming, Meng Yuanyuan, Xu Changxiang et al. Fault diagnosis model for power transformer based on Support Vector Machine and dissolved gas analysis[J]. Proceedings of the CSEE, 2003, 23(7): 88-92.
16. Zhai Yongjie, Han Pu, Wang Dongfeng et al. Risk function based SVM algorithm and its application in slight malfunction diagnosis[J]. Proceedings of the CSEE, 2003, 23(9): 198-203.
17. Xu Tao, He Renmu, Wang Peng et al. Power system transient stability assessment based on statistical learning theory[J]. Proceedings of the CSEE, 2003, 23(11): 51-55.