Abstract
Complex power quality disturbances (CPQDs) classification can be regarded as a typical application of multi-label (ML) learning. In this study, we propose a new recognition method for CPQDs based on S-transform (ST) and a hybrid kernel function-based extreme learning machine (ELM) for ML learning (HKEML). The signal processing techniques S-transform is utilized to extract the distinctive features of the CPQDs. A novel ML classifier called HKEML is constructed by combining hybrid kernel function-based multiclass ELM and a thresholding learning method-based kernel ELM. Finally, a test study was conducted using Matlab synthetic signals and real signals sampled from a three-phase standard source under different noise conditions. Compared with several recent state-of-the-art ML learning algorithms, HKEML achieved better classification performance but with greatly superior computational speed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
IEEE recommended practice for monitoring electric power quality. IEEE Std 1159-2009 (Revision of IEEE Std 1159-1995), c1–81 (2009)
Jurado, F., Saenz, J.R.: Comparison between discrete STFT and wavelets for the analysis of power quality events. Electr. Power Syst. Res. 62(3), 183–190 (2002)
Khokhar, S., Zin, A.A.M., Memon, A.P., Mokhtar, A.S.: A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network. Measurement 95, 246–259 (2017)
Zhang, S.Q., Li, P., Zhang, L.G., Li, H.J., Jiang, W.L., Hu, Y.T.: Modified S transform and elm algorithms and their applications in power quality analysis. Neurocomputing 185, 231–241 (2016)
Camarena-Martinez, D., Valtierra-Rodriguez, M., Perez-Ramirez, C.A., Amezquita-Sanchez, J.P., Romero-Troncoso, R.D., Garcia-Perez, A.: Novel downsampling empirical mode decomposition approach for power quality analysis. IEEE Trans. Industr. Electron. 63(4), 2369–2378 (2016)
Ozgonenel, O., Yalcin, T., Guney, I., Kurt, U.: A new classification for power quality events in distribution systems. Electr. Power Syst. Res. 95, 192–199 (2013)
Biswal, M., Dash, P.K.: Measurement and classification of simultaneous power signal patterns with an s-transform variant and fuzzy decision tree. IEEE Trans. Industr. Inf. 9(4), 1819–1827 (2013)
Shamachurn, H.: Assessing the performance of a modified S-transform with probabilistic neural network, support vector machine and nearest neighbour classifiers for single and multiple power quality disturbances identification. Neural Comput. Appl. 31, 1041–1060 (2017)
He, S.F., Li, K.C., Zhang, M.: A real-time power quality disturbances classification using hybrid method based on S-transform and dynamics. IEEE Trans. Instrum. Meas. 62(9), 2465–2475 (2013)
Li, J.M., Teng, Z.S., Tang, Q., Song, J.H.: Detection and classification of power quality disturbances using double resolution S-transform and DAG-SVMS. IEEE Trans. Instrum. Meas. 65(10), 2302–2312 (2016)
Valtierra-Rodriguez, M., Romero-Troncoso, R.D., Osornio-Rios, R.A., Garcia-Perez, A.: Detection and classification of single and combined power quality disturbances using neural networks. IEEE Trans. Ind. Electron. 61(5), 2473–2482 (2014)
Roy: Bayesian network approach in the classification of complex power quality disturbances
Biswal, M., Dash, P.K.: Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier. Digit. Signal Process. 23(4), 1071–1083 (2013)
Manikandan, M.S., Samantaray, S.R., Kamwa, I.: Detection and classification of power quality disturbances using sparse signal decomposition on hybrid dictionaries. IEEE Trans. Instrum. Meas. 64(1), 27–38 (2015)
Singh, U., Singh, S.N.: Application of fractional fourier transform for classification of power quality disturbances. IET Sci. Meas. Technol. 11(1), 67–76 (2017)
Kubendran, P., Kumar, A., Loganathan, A.K.: Detection and classification of complex power quality disturbances using S-transform amplitude matrix-based decision tree for different noise levels. Int. Trans. Electr. Energy Syst. 27(4), e2286 (2017)
Dalai, S., Dey, D., Chatterjee, B., Chakravorti, S., Bhattacharya, K.: Cross-spectrum analysis-based scheme for multiple power quality disturbance sensing device. IEEE Sens. J. 15(7), 3989–3997 (2015)
Borrs, M.D., Bravo, J.C., Montao, J.C.: Disturbance ratio for optimal multi-event classification in power distribution networks. IEEE Trans. Ind. Electron. 63(5), 3117–3124 (2016)
Thirumala, K., Maganuru, S.P., Jain, T., Umarikar, A.: Tunable-Q wavelet transform and dual multiclass SVM for online automatic detection of power quality disturbances. IEEE Trans. Smart Grid PP(99), 1 (2016)
Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognit. 40(7), 2038–2048 (2007)
Zhou, L., Guan, C., Lu, W.: Application of multi-label classification method to catagorization of multiple power quality disturbances. Proc. CSEE 31(4), 45–50 (2011)
Zhigang, L., Yan, C., Wenhui, L.: A classification method for complex power quality disturbances using EEMD and rank wavelet SVM. IEEE Trans. Smart Grid 6(4), 1678–1685 (2015)
Sun, X., Xu, J.T., Jiang, C.M., Feng, J., Chen, S.S., He, F.J.: Extreme learning machine for multi-label classification. Entropy 18(6), 225 (2016)
Stockwell, R.G., Mansinha, L., Lowe, R.P.: Localization of the complex spectrum: the S transform. IEEE Trans. Signal Process. 44(4), 998–1001 (1996)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. B Cybern. 42(2), 513–529 (2012)
Smits, G.F., Jordaan, E.M.: Improved SVM regression using mixtures of kernels. In: 2002 Proceedings of the 2002 International Joint Conference on Neural Networks, IJCNN 2002, vol. 3, pp. 2785–2790. IEEE (2002)
Zhang, M.L., Pena, J.M., Robles, V.: Feature selection for multi-label naive Bayes classification. Inf. Sci. 179(19), 3218–3229 (2009)
Tan, R.H., Ramachandaramurthy, V.: Numerical model framework of power quality events. Eur. J. Sci. Res. 43(1), 30–47 (2010)
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
Zhao, C., Li, K., Xu, X. (2020). A Method Based on S-transform and Hybrid Kernel Extreme Learning Machine for Complex Power Quality Disturbances Classification. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-23307-5_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23306-8
Online ISBN: 978-3-030-23307-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)