Grid Operation and Maintenance Station Locating with Electricity Materials Based on Improved k-Means Algorithm

Article Preview

Abstract:

Under the grid integrated control management mode, operation and maintenance station location has been the key to ensure the stable operation of the grid system. Based on grid line cost minimization of operation and maintenance station and a k-means algorithm, an operation and maintenance station location model is established. Through the practical example, the model makes operation and maintenance station location rational and scientific under considering the operation and maintenance line materials cost between substations. The electricity materials play important roles in the study and some study will be put on it.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

357-360

Citation:

Online since:

August 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] J.C. LI, G.Y. LI, D.X. NIU, et al: Assessment of distribution substation site selection based on improved BP neural networks . East China Electric Power, Vol. 35(2007), pp.10-12.

Google Scholar

[2] W.W. LI, D.S. LUO, J.G. YAO, et al: A Substation Location Method of Urban Distribution Network Planning Based on Limited Areas Handling Firstly. Guangdong Electric Power, Vol. 20(2007), pp.14-19.

Google Scholar

[3] Y.F. DONG, Y.Q. YANG, J. SONG: Optimal planning of substation locating based on improved PSO algorithm. Relay, Vol. 36(2008), pp.32-35.

Google Scholar

[4] F.Q. PU, Substation Optimization Research Based on Divisional Weighted Voronoi Diagram. Tianjin University(2008).

Google Scholar

[5] J.Q. SU, H.F. XUE, H.L. ZHAN: K-means Initial Clustering Center Optimal Algorithm Based on Partitioning. Microelectronics & Computer, Vol. 26(2009), pp.8-11.

Google Scholar

[6] Z. WANG, G.Q. LIU, E.H. CHEN: A k-means Algorithm Based on Optimized Initial Center Points. Pattern Recognition and Artificial Intelligence, Vol. 22(2009), pp.299-304.

Google Scholar

[7] C. ZHANG, S.X. XIA: k-means Clustering Algorithm with improved Initial Center. Knowledge Discovery and Data Mining, Vol. 3(2009), pp.790-792.

DOI: 10.1109/wkdd.2009.210

Google Scholar

[8] F. YUAN, Z.Y. ZHOU, X. SONG: k-means Clustering Algorithm with Meliorated Initial Center . Computer Engineering, Vol. 33(2007), pp.65-66.

Google Scholar

[9] J.J. LU: A developed k-means method based on weighted Euclidean distance. Chinese Journal of Hospital Statistics, Vol, 15(2008), pp.9-12.

Google Scholar

[10] Y. ZHOU: Substation location factors. Yunnan Electric Power, Vol. 37(2009), p.69, 74.

Google Scholar