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Gis-based Distribution Network Assisted Planning Systems Research And Design

Posted on:2010-02-19Degree:MasterType:Thesis
Country:ChinaCandidate:J LiuFull Text:PDF
GTID:2208360275983347Subject:Detection Technology and Automation
Abstract/Summary:PDF Full Text Request
With the rapid development of urban construction, upgrading and updating the power network has become an extremely urgent task for the rapid growth of electrical load and the demand of high quality power supply. Thus, developing a computer aided planning system for distribution network can not only increase work efficiency but also improve the quality of planning work, which carries both academic and practical values.This paper introduces the fundamental principle of GIS-based distribution network computer aided planning system and overall design. System modules include basic GIS functions and planning and management functions. Basic GIS functions consist of map roam, map zoom, eagle eye control, distance measurement, information query and editing. Planning and management functions consist of load prediction, substation locating and sizing and distribution network planning.According to the actual conditions, this paper also makes some episode analysis. Load prediction can use different methods to forecast power load based on historical load data, compare the result using residual analysis, save data that with higher accuracy, and generate thematic maps of load distribution. Substation locating and sizing can realize auto locating and sizing, which obtained in goal function and variables selection. Distribution network planning can solve the issue of network planning using genetic algorithms, and formulate code scheme under the circumstance of network built is known, which can guarantee that the power network is radically distributed and can supply all the load points. The effectiveness of the system is shown through case analysis.
Keywords/Search Tags:Distribution Network Planning, Geographic Information System, Load Forecasting, Substation Locating and Sizing, Genetic Algorithm
PDF Full Text Request
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