Font Size: a A A

Research On 10kV Distribution Network Optimal Planning Of Shi-Hezi

Posted on:2008-11-19Degree:MasterType:Thesis
Country:ChinaCandidate:Z C ZhangFull Text:PDF
GTID:2132360215995472Subject:Agricultural mechanization project
Abstract/Summary:PDF Full Text Request
The load of urban is increasing rapidly because of the national economy speedy development. Because the development of distribution network lags ,the problem which was old equipment, power insufficiency, illogical network connection and low reliability were outstanding. So all of power system developed the rebuild of urban network recently. Some power company had acquired primary effect. Many problems in rebuild of urban power grid are important. So many corporations are groping the method of rebuild. This is the background of the thesis.The thesis discussed the fundamental which is voltage grade, power supply reliability, connection mode, transformer load efficiency and power equipment. Put forward the principle of rebuild and planning to 10kV distribution system, it has instructional meaning to rebuild of Shihezi distribution network.In view of the radiation pattern power distribution network characteristic, the paper studied the load forecast methods which suits the power distribution network. In earnest the paper studies the application of grey theory in power load forecast,further studies the mathematics methods of error analysis. Basing on studying the theory and correlative data we established Shihezi's load forecast target.The network planning is an important content in distribution network layout rebuild. Therefore the paper discussed and applied the mathematics optimized plan method to mark out the network frame. Under considering each kinds of restricted conditions including network construct, voltage descend, line loss and reliability. Establishing optimized distribution network layout model and elaborating solving the model in the thought of genetic algorithm.
Keywords/Search Tags:Distribution Network, Load forecast, optimized planning, genetic algorithm
PDF Full Text Request
Related items