A New Multi-Method Combination Forecasting Model for ESDD Predicting
Haiyan SHUAI, Qingwu GONG
DOI: 10.4236/epe.2009.12015   PDF   HTML     5,534 Downloads   8,890 Views   Citations


Equal Salt Deposit Density (ESDD) is a main factor to classify contamination severity and draw pollution distribution map. The precise ESDD forecasting plays an important role in the safety, economy and reliability of power system. To cope with the problems existing in the ESDD predicting by multivariate linear regression (MLR), back propagation (BP) neural network and least squares support vector machines (LSSVM), a nonlinear combination forecasting model based on wavelet neural network (WNN) for ESDD is proposed. The model is a WNN with three layers, whose input layer has three neurons and output layer has one neuron, namely, regarding the ESDD forecasting results of MLR, BP and LSSVM as the inputs of the model and the observed value as the output. In the interest of better reflection of the influence of each single forecasting model on ESDD and increase of the accuracy of ESDD prediction, Morlet wavelet is used to con-struct WNN, error backpropagation algorithm is adopted to train the network and genetic algorithm is used to determine the initials of the parameters. Simulation results show that the accuracy of the proposed combina-tion ESDD forecasting model is higher than that of any single model and that of traditional linear combina-tion forecasting (LCF) model. The model provides a new feasible way to increase the accuracy of pollution distribution map of power network.

Share and Cite:

H. SHUAI and Q. GONG, "A New Multi-Method Combination Forecasting Model for ESDD Predicting," Energy and Power Engineering, Vol. 1 No. 2, 2009, pp. 94-99. doi: 10.4236/epe.2009.12015.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Y. Hu, “The analysis of ‘2.22 pollution flashover of network’ and measure for anti-pollution flashover,” High Voltage Engineering, Vol. 27, pp. 30–32, April 2001.
[2] Y. Liu and J. K. Wang, “Analysis of large area pollution flashover occurred in Shaanxi power network on Dec.18, 2000 and preventative measures for similar accidents,” Power System Technology, Vol. 26, pp. 82–85, June 2002.
[3] Pollution Classification and External Insulation Selection for Electrical Power System, State Grid Standard Q/ GDW152-2006, December 2006.
[4] A. S. Almad, H. Ahmad, M. A. Salam, and Saad Ahmad, “Regression technique for prediction of salt contamination severity on high voltage insulators,” in Annual Report Conference on Electrical Insulation and Dielectric Phenomena, pp. 218–221, 2000.
[5] A. S. Almad, H. Ahmad, M. A. Salam, T. Tamsir, Z. Buntat, and M. W. Mustafa, “Prediction of salt contamination on high voltage insulators in rainy season using regression technique,” Proceedings of TENCON, Vol. 3, pp. 184–189, 2000.
[6] M. A. Salam, S. M. Ai-Alawi, and A. A Maqrashi, “Prediction of equivalent deposit density of contaminated glass plates using artificial neural networks,” Journal of Electrostatics, Vol. 66, pp. 526–530, May 2008.
[7] A. S. Almad, P. S. Ghosh, H. Ahmad, and S. A .K. Aljunid, “Assessment of ESDD on high-voltage insulators using artificial neural network,” Electric Power System Research, Vol. 72, pp. 131–136, December 2004.
[8] S. B. Jiao, D. Liu, G. Zheng, and Q. Zhang, “Forecasting the ESDD of insulator based on least squares support vector machine,” Proceedings of the CSEE, Vol. 26, pp. 149–153, January 2006.
[9] F. H. Shu and W. X. Zhang, “A prediction model for insulator’s ESDD based on least square support vector machine,” High Voltage Apparatus, Vol. 44, pp. 420–423, October 2008.
[10] J. M. Bate and C. W. J. Granger, “The combination of forecastings,” Operational Research Quarterly, Vol. 20, pp. 451–468, 1969.
[11] Y. Liu. “Mathematical model of multivariable linear regression,” Journal of Shenyang Institute of Engineering, Vol. 1, pp. 128–129, Junuary 2005.
[12] J. A. K. Suykens and J. Vandewalle. “Least squares support vector machine classifiers,” Neural Process Letters, Vol. 9, pp. 293–300, 1999.
[13] J. A. K. Suykens, L. Lukas, P. Van Dooren, B. Demoor, and J. Vandewalle, “Least squares support vector machine classifiers: A large scale algorithm,” ECCTD’99 European Conference on Circuit Theory and Design, pp. 293–300, 1999.
[14] Y. K. Ma and X. W. Tang, “Research on the problem of optimizing linear combination forecasting model,” System Engineering – Theory & Practice, Vol. 9, pp. 110– 123, September 1998.
[15] X. H. Wen and M. J. Niu, “A new nonlinear combined forecasting method on the basis of neural networks,” System Engineering-Theory & Practice, Vol. 12, pp. 66–72, December 1994.
[16] S. X. Li, “Wavelet transform and its applications,” Higher Education Press, Beijing, pp. 11–15, 1997.
[17] Q. X. Cuo and L. Liu, “Driving force prediction for inclusion complexation of 2-cyclodextrim with benzene derivatives by a wavelet neural network,” Chemical Physics Letters, Vol. 290, pp. 514–518, 1998.
[18] H. F. Liang, G. Y. Tu, and H. W. Tang, “Application of genetic algorithm neural network for short term load forecasting of power system,” Power System Technology, Vol. 25, pp. 49–53, January 2001.

Copyright © 2022 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.