Harmonic Source Model Based on Support Vector Machine
To analyze the harmonics in power system efficiently, a new harmonic source model is proposed in this paper. And this new model takes advantage of support vector machine (SVM) theory to find the relationship between the harmonic current and all voltage components. Then a comparison between the linear regressive model and nonlinear regressive models with different kernel functions has been made. The computer simulation has revealed that the model implemented by the nonlinear regression with Polynomial kernel is more precise, and is superior to other regressions.
KeywordsSupport Vector Machine Power System Support Vector Regression Testing Error Polynomial Kernel
Unable to display preview. Download preview PDF.
- 1.Yong, Z., Tao, Z., Jianhua, L., Daozhi, X.: A new simplified harmonic source Model [J]. Proceedings of the CSEE 22(4), 46–51 (2002)Google Scholar
- 2.Yong, Z., Haozhong, C., Nacheng, G., Guangbing, H.: Generalized growing and pruning RBF neural network based harmonic source modeling [J]. Proceedings of the CSEE 25(16), 42–46 (2005)Google Scholar
- 3.Task Force on Harmonic Modelling and Simulation. Modelling and simulation of the propagation of harmonics in electric power networks part I: Concepts, Models, and Simulation Techniques[J]. IEEE Trans. Arrillaga J. et al., Power system harmonics. John Wiley & Sons (1985)Google Scholar
- 4.Ma, L., Liu, K., Li, L.: Harmonic and inter-harmonic detecting based on support vector machine. In: Transmission and Distribution Conference and Exhibition: Asia and Pacific, 2005, IEEE/PES, August 15-18, 2005, pp. 1–4 (2005)Google Scholar
- 6.Smola, A.J., Schölkopf, B., Müller, K.-R.: General cost functions for support vector regression. In: Proc. 9th Australian Conf. Neural Networks, Brisbane, Australia, pp. 79–83 (1998)Google Scholar
- 7.Cristianini, N., Shawe-Taylor, J.: An introduction to support vector machines and other kernel-based learning methods[M]. Cambridge University Press, Cambridge (2000)Google Scholar
- 12.Wang, L.P.: Support Vector Machines: Theory and Application. Springer, Berlin, Heidelberg, New York (2005)Google Scholar