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The Method Of Reducing Network Loss Design And Achievement Based On Distribution Network Reconfiguration

Posted on:2015-04-30Degree:MasterType:Thesis
Country:ChinaCandidate:J L WuFull Text:PDF
GTID:2272330431981230Subject:Electronic and communication engineering
Abstract/Summary:PDF Full Text Request
Along with the rapid development of the domestic economy, and the electricity power market gradually establishing and perfect, people have higher request to the power supply quality and operating reliability and stability under the growing electricity demand condition. Meanwhile, with the development of information technology, computer technology and automation technology, it has been well positioned to become the intellectualization of power grid. As distribution automation play a key role in the construction of smart grid, not only reconfiguration of distribution network is an important research field of network automation, but a powerful tool to optimize the distribution system operation.In this paper, differential evolution algorithm, modified differential evolution and simulated annealing method were adopted for distribution network reconfiguration, taking the minimal active power loss as objective function. Then we carried out simulation to the network based on these three methods, respectively, the results demonstrate that the simulated annealing algorithm has higher efficiency than the other two methods in computing speed, the optimal solution probability. Taking IEEE33-node system as an example, compared with the three network reconfiguration algorithms in computing time, optimized iterative times and network loss. Aimed at the access number, location and capacity of the distributed generation, its impact on the feasible solutions, network loss, the node voltage levels and supply reliability has been analyzed. Moreover, we study the influence on the optimal solution for the network reconfiguration with distributed power under fault and non-fault conditions.
Keywords/Search Tags:distribution network reconfiguration, simulated annealing algorithm, distributed generation
PDF Full Text Request
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