A KPCA and SVR Based Dynamic State Estimation Method for Power System

Dynamic State Estimation (DSE) for power system considers statistical characters of systemic state variables in past period, has functions of state estimation and forecasting. This paper proposes a new method for state estimation problem in power systems based on Kernel Principle Component Analysis (KPCA) and Support Vector Regression (SVR). Firstly, the KPCA can extract the nonlinear relationship between original inputs from SCADA system to make data compression and feature extraction. KPCA is closely related to methods applied in Support Vector Regression (SVR). Then, the extracted principal data are used as inputs of SVM in order to forecast systemic state variables. Applying proposed system to IEEE14 data, the experiment results show that KPCA-SVR features high learning speed, good approximation and generalization ability compared with SVR.

[1]  Lijuan Cao,et al.  Combining KPCA with support vector machine for time series forecasting , 2003, 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings..

[2]  Jong-Koo Park,et al.  The concept and design of dynamic state estimation , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[3]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[4]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[5]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[6]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[7]  Richard Weber,et al.  A wrapper method for feature selection using Support Vector Machines , 2009, Inf. Sci..

[8]  Lijuan Cao,et al.  A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine , 2003, Neurocomputing.

[9]  Avinash Kumar Sinha,et al.  Dynamic state estimator using ANN based bus load prediction , 1999 .

[10]  Yuanyuan Wang,et al.  A rough margin based support vector machine , 2008, Inf. Sci..

[11]  A.H. Abdullah,et al.  Support Vector Machine Based Approach for State Estimation of Iraqi Super Grid Network , 2008, 2008 Workshop on Power Electronics and Intelligent Transportation System.

[12]  Hong Qiao,et al.  Associated evolution of a support vector machine-based classifier for pedestrian detection , 2009, Inf. Sci..