配用電系統(tǒng)高級量測體系與數(shù)據(jù)應(yīng)用方法研究
發(fā)布時間：2018-11-18 18:32
【摘要】：在電力領(lǐng)域“大數(shù)據(jù)時代”到來的背景下,本文針對配用電系統(tǒng),按照數(shù)據(jù)采集、數(shù)據(jù)清洗、數(shù)據(jù)應(yīng)用三層邏輯結(jié)構(gòu),建立了一套應(yīng)用理論和工程實(shí)踐方法。數(shù)據(jù)采集指的是基于IPv6的高級量測體系架構(gòu)和組網(wǎng)技術(shù)研究。高級量測體系是一套完整的體系,可實(shí)現(xiàn)對電力消費(fèi)者的能源消費(fèi)的進(jìn)行量測、讀取、存儲和分析功能,包括配套的的傳感、通信和軟件系統(tǒng)。IPv6技術(shù)是互聯(lián)網(wǎng)的發(fā)展方向,提出了將IPv6技術(shù)與高級量測體相結(jié)合的組網(wǎng)方案。針對基于無線傳感網(wǎng)絡(luò)技術(shù)的智能電表,為了更好地將IPv6技術(shù)進(jìn)行部署,對兩者的互聯(lián)方式進(jìn)行了研究,提出了將整個智能電表網(wǎng)絡(luò)虛擬為一個IPv6網(wǎng)絡(luò)接入點(diǎn)的設(shè)計思想,并給出了通信組網(wǎng)的具體實(shí)現(xiàn)方法。使得6Lo WPAN無線智能電表網(wǎng)絡(luò)能夠直接接入IPv6有線網(wǎng)絡(luò),實(shí)現(xiàn)兩個網(wǎng)絡(luò)間任意點(diǎn)與點(diǎn)的互聯(lián),將配用電系統(tǒng)“最后一公里”互聯(lián)網(wǎng)化,相關(guān)組網(wǎng)方案已經(jīng)用于試點(diǎn)項目,可為后續(xù)的大數(shù)據(jù)應(yīng)用提供了技術(shù)支持。數(shù)據(jù)清洗指的是針對配電變壓器的數(shù)據(jù)清洗方法研究。配用電變壓器作為配用電系統(tǒng)的關(guān)鍵環(huán)節(jié),由于分布面廣、網(wǎng)絡(luò)結(jié)構(gòu)復(fù)雜、運(yùn)行環(huán)境惡劣等客觀條件的制約,其數(shù)據(jù)在實(shí)際運(yùn)行中常存在大量異常和缺失情況。為了解決上述問題,以現(xiàn)實(shí)中的配電變壓器數(shù)據(jù)為研究樣本,首先分析了配電變壓器數(shù)據(jù)異常和數(shù)據(jù)丟失兩類情況的產(chǎn)生原因,之后采用小樣本數(shù)據(jù)進(jìn)行算法研究,針對配電變壓器數(shù)據(jù)異常問題提出基于Kernel Smoothing技術(shù)的異常數(shù)據(jù)識別方法,針對配電變壓器數(shù)據(jù)丟失問題提出了基于Pearson相關(guān)系數(shù)與回歸模型相結(jié)合的缺失數(shù)據(jù)恢復(fù)方法。為驗證所提方法能夠滿足TB級數(shù)據(jù)的運(yùn)算效率,采用6臺服務(wù)器搭建Spark并行計算結(jié)構(gòu),對大樣本的配電變壓器數(shù)據(jù)進(jìn)行了異常數(shù)據(jù)的識別和缺失數(shù)據(jù)的恢復(fù),相關(guān)結(jié)果證明了所提方法的實(shí)際價值。該數(shù)據(jù)清洗方法不光適用于配電變壓器數(shù)據(jù),可應(yīng)用于解決配用電系統(tǒng)的其他數(shù)據(jù)問題。數(shù)據(jù)清洗是數(shù)據(jù)應(yīng)用的第一步,為開發(fā)更多數(shù)據(jù)應(yīng)用方法開辟了道路。數(shù)據(jù)應(yīng)用指的在前面數(shù)據(jù)采集和數(shù)據(jù)清洗的基礎(chǔ)上,對配用電系統(tǒng)數(shù)據(jù)應(yīng)用方法進(jìn)行研究。高級量測系統(tǒng)采集的數(shù)據(jù)是配用電系統(tǒng)的基礎(chǔ)數(shù)據(jù),具有多源異構(gòu)、規(guī)模巨大的特點(diǎn),呈現(xiàn)出大數(shù)據(jù)的特征。利用智能電表數(shù)據(jù),進(jìn)行數(shù)據(jù)挖掘,結(jié)合電氣專業(yè)基本知識,采用相關(guān)分析等統(tǒng)計學(xué)方法,可以為電力行業(yè)解決實(shí)際工程問題,為各種利益相關(guān)方創(chuàng)造效益。提出了基于智能電表數(shù)據(jù)的用戶與臺區(qū)關(guān)系拓?fù)湫ｒ灧椒ㄑ芯?可為配電網(wǎng)運(yùn)行維護(hù)節(jié)省大量的投入成本,實(shí)現(xiàn)資產(chǎn)管理智能化,文中以現(xiàn)實(shí)數(shù)據(jù)作為算例,證明了方法的實(shí)際價值。
[Abstract]:Under the background of "big data era" in electric power field, this paper establishes a set of application theory and engineering practice method according to the three-layer logical structure of data acquisition, data cleaning and data application. Data acquisition refers to the research of advanced measurement architecture and networking technology based on IPv6. Advanced measurement system is a complete system, which can measure, read, store and analyze the energy consumption of electric power consumers, including the matching sensing, communication and software systems. IPv6 technology is the development direction of the Internet. A scheme of combining IPv6 technology with advanced measurement is proposed. Aiming at the intelligent meter based on wireless sensor network technology, in order to better deploy IPv6 technology, the interconnection mode between them is studied, and the design idea of virtual IPv6 network access point is put forward. The realization method of communication network is also given. The 6Lo WPAN wireless intelligent meter network can be directly connected to the IPv6 wired network, to realize the interconnection of any point and point between the two networks, to make the distribution system "the last kilometer" of the Internet, and the related networking scheme has been used in the pilot project. Can provide technical support for subsequent big data application. Data cleaning refers to the study of data cleaning method for distribution transformers. As the key link of power distribution system, due to the restriction of objective conditions, such as wide distribution, complex network structure and poor operating environment, there are often a large number of anomalies and deficiencies in the actual operation of the distribution transformer. In order to solve the above problem, taking the distribution transformer data in reality as the research sample, this paper first analyzes the causes of the abnormal data and the data loss of the distribution transformer, and then uses the small sample data to carry on the algorithm research. In order to solve the problem of abnormal data of distribution transformer, a method of identifying abnormal data based on Kernel Smoothing technology is put forward, and a method of recovering missing data based on Pearson correlation coefficient and regression model is proposed to solve the problem of data loss of distribution transformer. In order to verify that the proposed method can meet the operational efficiency of TB level data, six servers are used to construct the Spark parallel computing structure, and the abnormal data and the missing data are identified and recovered from the large sample distribution transformer data. The results show that the proposed method is of practical value. The data cleaning method is not only suitable for distribution transformer data, but also can be used to solve other data problems of distribution system. Data cleaning is the first step of data application, which opens the way for developing more data application methods. Data application refers to the data application method of distribution system based on data acquisition and data cleaning. The data collected by the advanced measurement system is the basic data of the power distribution system. It has the characteristics of multi-source heterogeneity and huge scale, showing the character of big data. Using intelligent meter data, data mining, combined with electrical professional basic knowledge, using statistical methods such as correlation analysis, can solve practical engineering problems for power industry, and create benefits for various stakeholders. In this paper, the topology checking method of user-to-station relationship based on intelligent ammeter data is proposed, which can save a lot of input cost for distribution network operation and maintenance, and realize intelligent asset management. The actual data is taken as an example in this paper. The practical value of the method is proved.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM76
[Abstract]:Under the background of "big data era" in electric power field, this paper establishes a set of application theory and engineering practice method according to the three-layer logical structure of data acquisition, data cleaning and data application. Data acquisition refers to the research of advanced measurement architecture and networking technology based on IPv6. Advanced measurement system is a complete system, which can measure, read, store and analyze the energy consumption of electric power consumers, including the matching sensing, communication and software systems. IPv6 technology is the development direction of the Internet. A scheme of combining IPv6 technology with advanced measurement is proposed. Aiming at the intelligent meter based on wireless sensor network technology, in order to better deploy IPv6 technology, the interconnection mode between them is studied, and the design idea of virtual IPv6 network access point is put forward. The realization method of communication network is also given. The 6Lo WPAN wireless intelligent meter network can be directly connected to the IPv6 wired network, to realize the interconnection of any point and point between the two networks, to make the distribution system "the last kilometer" of the Internet, and the related networking scheme has been used in the pilot project. Can provide technical support for subsequent big data application. Data cleaning refers to the study of data cleaning method for distribution transformers. As the key link of power distribution system, due to the restriction of objective conditions, such as wide distribution, complex network structure and poor operating environment, there are often a large number of anomalies and deficiencies in the actual operation of the distribution transformer. In order to solve the above problem, taking the distribution transformer data in reality as the research sample, this paper first analyzes the causes of the abnormal data and the data loss of the distribution transformer, and then uses the small sample data to carry on the algorithm research. In order to solve the problem of abnormal data of distribution transformer, a method of identifying abnormal data based on Kernel Smoothing technology is put forward, and a method of recovering missing data based on Pearson correlation coefficient and regression model is proposed to solve the problem of data loss of distribution transformer. In order to verify that the proposed method can meet the operational efficiency of TB level data, six servers are used to construct the Spark parallel computing structure, and the abnormal data and the missing data are identified and recovered from the large sample distribution transformer data. The results show that the proposed method is of practical value. The data cleaning method is not only suitable for distribution transformer data, but also can be used to solve other data problems of distribution system. Data cleaning is the first step of data application, which opens the way for developing more data application methods. Data application refers to the data application method of distribution system based on data acquisition and data cleaning. The data collected by the advanced measurement system is the basic data of the power distribution system. It has the characteristics of multi-source heterogeneity and huge scale, showing the character of big data. Using intelligent meter data, data mining, combined with electrical professional basic knowledge, using statistical methods such as correlation analysis, can solve practical engineering problems for power industry, and create benefits for various stakeholders. In this paper, the topology checking method of user-to-station relationship based on intelligent ammeter data is proposed, which can save a lot of input cost for distribution network operation and maintenance, and realize intelligent asset management. The actual data is taken as an example in this paper. The practical value of the method is proved.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM76
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