本文選題：智能變電站 + 選址��； 參考：《吉林大學(xué)》2017年碩士論文

【摘要】：智能變電站選址一直是電力行業(yè)研究的重要課題之一。從電力角度講,變電站的站址優(yōu)劣直接影響未來電力系統(tǒng)的供電質(zhì)量和運(yùn)行經(jīng)濟(jì)性。以往變電站選址多使用屬性數(shù)據(jù)(如變電站容量),對(duì)空間數(shù)據(jù)分析較少,更不論利用空間大數(shù)據(jù)進(jìn)行智能變電站的選址分析。而在變電站選址過程中,需要應(yīng)用到遙感數(shù)據(jù)、環(huán)境數(shù)據(jù)、電子表數(shù)據(jù)等各類數(shù)據(jù),尤其是需要對(duì)大量空間數(shù)據(jù)進(jìn)行分析。這些數(shù)據(jù)的數(shù)據(jù)量巨大,在分析時(shí)單機(jī)模式無法滿足其分析要求,所以需引入大數(shù)據(jù)分析模式,采用分布式分析進(jìn)行變電站選址。本研究主要包括以下五點(diǎn)內(nèi)容:1.深入分析變電站選址影響因素,分析、總結(jié)變電站選址主要受經(jīng)濟(jì)因素、地形地貌因素、國土資源與自然災(zāi)害因素、自然資源因素、人文社會(huì)因素五大類因素制約。根據(jù)分析出的影響因素制定可量化的智能變電站選址指標(biāo)體系,并對(duì)體系中的指標(biāo)合理賦值。2.綜合考慮智能變電站選址模型中的經(jīng)濟(jì)模型和空間位置模型,采用層次分析法建立可擴(kuò)展的、科學(xué)的基于大數(shù)據(jù)的智能變電站的選址模型。3.設(shè)計(jì)智能變電站選址系統(tǒng)的大數(shù)據(jù)智能分析系統(tǒng)框架、分布式存儲(chǔ)、分布式計(jì)算框架,設(shè)計(jì)系統(tǒng)的功能模型及每一個(gè)子功能,并給出智能變電站空間大數(shù)據(jù)分析或挖掘的數(shù)學(xué)算法。利用Eclipse、Web Storm作為開發(fā)工具,利用目前世界上最新、最先進(jìn)的Arc GIS Tools for Hadoop與大數(shù)據(jù)平臺(tái)交互,采用Arc GIS Server 10.5作為空間大數(shù)據(jù)分析平臺(tái),采用Java、Java Script、AIR等編程實(shí)現(xiàn)B/S模式的基于大數(shù)據(jù)的智能變電站選址系統(tǒng)。采用長(zhǎng)春地區(qū)部分?jǐn)?shù)據(jù),對(duì)基于大數(shù)據(jù)的智能變電站選址模型進(jìn)行測(cè)試。分析結(jié)果表明,模型是可行的、合理的�；诖髷�(shù)據(jù)的智能變電站的選址系統(tǒng)為智能變電站選址適應(yīng)大數(shù)據(jù)時(shí)代發(fā)展需求提供新的解決方案,為智能變電站信息化、智能化、高效化提供支持。
[Abstract]:Intelligent substation location has been one of the important research topics in power industry. From the power point of view, the substation site directly affects the power supply quality and operation economy of the future power system. In the past, attribute data (such as substation capacity) were used in substation siting, and spatial data were less analyzed, and spatial big data was used to analyze the location of intelligent substation. In the process of substation location, it needs to be applied to remote sensing data, environmental data, electronic table data and other data, especially to a large number of spatial data analysis. The data volume of these data is so large that the single machine mode can not meet the requirements of the analysis, so it is necessary to introduce the big data analysis mode and adopt distributed analysis to locate the substation. This research mainly includes the following five points: 1. This paper analyzes the influence factors of substation location, analyzes and summarizes that substation location is mainly restricted by five kinds of factors: economic factors, landform factors, land resources and natural disasters factors, natural resources factors, humanities and social factors. According to the analysis of the influencing factors, a quantifiable intelligent substation location index system is established, and the index in the system is assigned to the reasonable value of .2. Considering the economic model and spatial location model of intelligent substation location model, an extensible and scientific intelligent substation location model based on big data. 3 is established by analytic hierarchy process (AHP). The big data intelligent analysis system framework, distributed storage, distributed computing framework, function model and each sub-function of the intelligent substation location system are designed. A mathematical algorithm for spatial big data analysis or mining of intelligent substation is presented. Using Eclipse Arc Storm as the development tool, using the newest and most advanced Arc GIS tools for Hadoop to interact with the big data platform, and using Arc GIS Server 10.5 as the spatial big data analysis platform. The intelligent substation location system based on big data is implemented by Java script and air. The intelligent substation location model based on big data is tested with some data in Changchun area. The results show that the model is feasible and reasonable. The intelligent substation location system based on big data provides a new solution for the intelligent substation location to meet the needs of the development of the big data era, and provides the support for the intelligent substation information, intelligence and high efficiency.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM63;TM76

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王雪瓊;熊s，

本文編號(hào)：1987523