Abstract:Blind source separation(BSS) can be applied to estimate load profiles using only a small set of active line flow measurements without prior knowledge of the electric network model parameters or topology. In this paper Kernel independent component analysis is used to estimate electric load profiles which use contrast functions based on canonical correlations in a reproducing Kernel Hilbert space. The proposed approach is demonstrated for the IEEE-14 system to estimate eight active load profiles. The results from Kernel independent component analysis algorithm have lower estimation errors and larger correlation coefficients compared with those from independent component analysis algorithm.
刘瑜, 韦琦, 魏新劳. 基于盲源分离技术的电力用户负荷曲线估计[J]. 电工技术学报, 2009, 24(10): 160-164.
Liu Yu, Wei Qi, Wei Xinlao. Electric Power Load Profile Estimation Based on Blind Source Separation. Transactions of China Electrotechnical Society, 2009, 24(10): 160-164.
[1] Alsac O, Vempati N, Stott B, et al. Generalized state estimation[J]. IEEE Trans. on Power Syst., 1998, 13(8): 1069-1075. [2] Joint US-Canada Power System Outage Task Force. Final Report on the August 14th Blackout in the United States and Canada. US DOE[OL]. Available: http:// reports. energy. gov/, April 5, 2004. [3] 邰能灵, 侯志俭, 李涛, 等. 基于小波分析的电力系统短期负荷预测方法[J]. 中国电机工程学报, 2003, 23(1): 45-50. [4] Ghosh A K, Lubkeman D L, Jones R H.Load modeling for distribution circuit state estimation[J]. IEEE Trans. on Power Delivery, 1997, 12(2): 999-1005. [5] Liao Huaiwei, Niebur Dagmar. Load profile estimation in electric transmission networks using independent component analysis[J]. IEEE Transactions on Power Systems, 2003, 18(2): 707-715. [6] Niebur D, Rotolo A, Mazzoleni F. Load profile separation using independent component analysis[C]. Proc. Int. Con. Intell. Syst. Applicat. Power Syst., Budapest, Hungary, 2001. [7] Lu W, Rajapakse J C. Unique ICA solution by eliminating indeterminacy[C]. Proc. of the Int. Joint Conf. Neural Networks, Washington D. C., 2001, 7: 388-393. [8] Hyvarinen A, Karhunen J, Oja E. 独立成分分析[M]. 周宗潭, 董国华, 徐昕, 等译. 北京:电子工业出版社, 2007. [9] Comon P. Independent component analysis-a new concept? [J]. Signal Processing, 1994, 36: 287-314. [10] Hyvärinen A. Fast and robust fixed-point algorithms for independent component analysis[J]. IEEE Trans. on Neural Networks, 1999, 10(3): 626-634. [11] Hyvarinen A. Independent component analysis for time-dependent stochastic processes[C]. Proc. of the Int. Conf. Artificial Neural Networks, Skovde Sweden, 1998: 135-140. [12] Yang Jian, Gao Xiumei, Zhang David, et al. Kernel ICA:an alternative formulation and its application to face recognition [J]. Pattern Recognition, 2005, 38: 1784-1787. [13] Mika S, SchKolkopf B, Smola A, et al. Kernel PCA and de-noising in feature spaces[M]. MA: MIT Press, 1999. [14] Akaho S. A Kernel method for canonical correlation analysis[M]. Proceedings of the International Meeting of the Psychometric Society (IMPS2001). Tokyo: Springer-Verlag, 2001. [15] Harmeling S, Ziehe A, Kawanabe M, et al. Kernel feature spaces and nonlin-ear blind source separation [M]. MA: MIT Press, 2002. [16] SchÄolkopf B, Smola A J. Learning with Kernels[M]. MA: MIT Press, 2001. [17] New York ISO: Real Time Actual Load Data, From OASIS of NYISO. [OL]. Available: http://www. nyiso. com. [18] Zimmerman Ray D, Murillo-Sánchez Carlos E, Gan Deqiang. A MATLAB Power System Simulation Package[OL]. Available: http://www. pserc. cornell. edu/matpower.