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Study Of 10kv Distribution Network Planning

Posted on:2004-06-17Degree:MasterType:Thesis
Country:ChinaCandidate:F QianFull Text:PDF
GTID:2132360092981050Subject:Power system and its automation
Abstract/Summary:PDF Full Text Request
As the connection between transmission network and consumers facilities, distribution network has been paid more and more attentions about its planning. According to the statues of distribution network in our country, some parts of research work about the distribution network planning are studied in this paper as following:The first part is about the load forecasting for the distribution network planning. Firstly, the content and the meaning of the load forecasting are introduced in the paper. The theory and models of spatial load forecasting algorithm are also studied in details. Then this algorithm is applied on the graphic information systems. During the load forecasting, some steps of the algorithm are improved. The sample shows that the spatial load forecasting algorithm is feasible and superior.In the second part, the topology of distribution network on the graphic platform is studied. This paper presents a fast tracking method of network topology based on a width priority search. Only depend on the node numbers of graphic devices and the status of switches, the method can deal with every status of network connectivity quickly.In the third part, this paper studies on the optimal planning of the distribution network. Firstly, the paper introduces the normal models and power flow calculation of the distribution network planning. Then, after expatiating on the genetic algorithm and the immune algorithm, this paper presents a new immune-genetic algorithm based on multiple populations and applies the algorithm on the 10KV distribution network planning. At last, the algorithm is proved to have a good search ability on the global solution space and a fast convergence speed by a sample.
Keywords/Search Tags:Distribution Network, Spatial Load Forecasting, Network Topology, Optimal Planning, Immune-Genetic Algorithm
PDF Full Text Request
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