Abstract
It’s difficult to classify multiple disturbances with the single disturbance classification method. This paper describes multiple disturbances and designs the classifier of multiple power quality (PQ) disturbances; the process of power quality disturbance classification can be divided into two-stage, feature extraction, and classification. This paper extracts features of disturbances with dq Transform, Wavelet Packet Transform (WPT) and S-Transform (ST), and combines them to reflect the characteristics of disturbances better. The design of binary tree Support Vector Machine (BT-SVM) with the concept of the class distance of the clustering analysis makes classifications intelligently. And dynamic event tree is proposed to make classifications of multiple disturbances. By these methods, disturbances can be classified fast and accurately. The results of simulation show that the classification method in this paper is able to classify multiple disturbances effectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xiao, X.: Power quality analysis and control. China Electric Power Press Beijing 44(2), 45–47 (2004). (in Chinese)
Zhang, X.: The research on the method of power quality disturbances detection and classification. North China Electric Power Univ. Beijing 1(1), 2–4 (2004). (in Chinese)
Zhou, L., Guan, C., Lu, W.: Application of multi-label classification method to the categorization of multiple power quality disturbances. Proc. CSEE 31(4), 45–47 (2011)
Li, Y.: Detection and analysis of transient power quality based on the S transform. Northeastern University, Shenyang, 1(1), 1–2 (2009). (in Chinese)
Ma, Z., Li, P., Yang, Y., et al.: Power quality detecting based on wavelet multi-resolution method. J. North China Electric Power Univ. 30(13), 6–9(2003). (in Chinese)
Qiao, Z., Sun, W.: A multi-class classifier based on the SVM decision tree. Comput. Appl. Soft. 11(227), 27–30 (2009). (in Chinese)
Katsaprakakis, D.A.J., Christakis, D.G., Zervos, A.: A power-quality measure. IEEE Trans. Power Deliv. 23(2), 553–561 (2008)
Tong, W., Song, X., Song, J.: Detection and classification of power quality disturbances based on wavelet packet decomposition and support vector machines. In: Proceedings of the 8th International Conference on Signal Processing, vol. 4, pp. 3015–3018 (2006). (in Chinese)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gao, Q., Pan, F., Yuan, F., Pan, J., Zhang, J., Zhang, Y. (2020). The Classification of Multiple Power Quality Disturbances Based on Dynamic Event Tree and Support Vector Machine. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-14118-9_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14117-2
Online ISBN: 978-3-030-14118-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)