OPEN ACCESS
According to the existing substation locating and sizing method due to the neglect of geographic factors which affect the accuracy of practical application, this article puts forward a method of substation locating and sizing based on geographic information system, the method combining the characteristics of county power grid and the geographical environment of substation, but also considering the effect of geographical factors such as terrain, landform, and density of population on investment cost, so that the results can play the biggest role in the practical application. Moreover, the application of the improved particle swarm algorithm in substation locating and sizing, taking into account the relationship between the individual and the overall situation, to further improve the location accuracy. The application of this method in a county in Shaanxi Province of substation location, confirmed that the method can effectively reduce the infeasible solutions, and eventually converges to the global optimal solution and make the substation planning results consistent with the actual requirements.
GIS, PSO, Site selection of transformer substation, Economical capacity of transformer substation
[1] Wang Chengshan, Wei Haiyang, Xiao Jun, et al. “Two-phase optimal planning for substation locating and sizing,” Automation of Electric Power Systems, vol. 29, nol. 4, pp. 62-66, 2005.
[2] Dai Hongwei, Yu Yinxin, Huang Chunhua, et al. “Optimal planning of distribution substation locations and sizes-model and algorithm,” International Journal of Electrical Power & Energy Systems, vol. 18, no. 6, pp. 353-357, 1196.
[3] El-Gallad A., El-Hawary M., Sallam A., et al. “Swarm-intelligently trained neural network for power transformer protection,” Proceedings of Conference on Electrical and Computer Engineering, Canada, 2001, pp. 265−269.
[4] Liu Ling, Yan Deng-jun, Gong Deng-cai, et al. “New method for short term load forecasting based on particle swarm optimization and fuzzy neural network,” Proceedings of the Chinese Society of Universities, vol. 18, no. 3, pp. 47−50, 2006.
[5] Shi Biao, Li Yu-xia, Yu Xin-hua and Yan Wang. “Short-term load forecast based on modified particle swarm optimizer and back propagation neural network model,” Journal of Computer Applications, vol. 29, no. 4, pp. 1036-1039, 2009.
[6] Omkar S. N., Mudigere D., Narayananaikg, et al. “Vector evaluated particle swarm optimization for multi-objective design optimization of composite structures,” Computers and Structures, vol. 86, no. ½, pp. 1-14, 2008.
[7] Xu Tong-yu and Sun Yan-hui. “Substation locating and sizing in rural power system based on GIS and modified differential evolution algorithm,” Power System Protection and Control, vol. 37, no. 22, 2009.