【摘要】：隨著電力系統(tǒng)規(guī)模的增大、量測技術(shù)的發(fā)展與成本下降,電力系統(tǒng)的數(shù)據(jù)量呈現(xiàn)快速增長趨勢,逐步具備了大數(shù)據(jù)特征。充分利用大數(shù)據(jù)來改善電力系統(tǒng)的規(guī)劃、運(yùn)行與控制已受到越來越廣泛重視,這樣如何評估大數(shù)據(jù)的質(zhì)量就是一個(gè)值得研究的重要問題。在數(shù)據(jù)質(zhì)量提高技術(shù)如數(shù)據(jù)清洗、數(shù)據(jù)整合、相似記錄檢測等方面,已有相當(dāng)多的研究報(bào)道。然而,在數(shù)據(jù)質(zhì)量的評估方面,研究工作還相當(dāng)初步。在此背景下,針對電力系統(tǒng)特征和電力大數(shù)據(jù)質(zhì)量特性,提出一種電力大數(shù)據(jù)質(zhì)量綜合評估方法。對大批量的評估總體,有時(shí)各類質(zhì)量的實(shí)際分布差別很大,那么采用組合的評估方法就比較合理,因此將熵權(quán)法和灰類評估法結(jié)合起來,在Hadoop平臺上構(gòu)建電力大數(shù)據(jù)質(zhì)量評估模型,該模型詳細(xì)地列出電力大數(shù)據(jù)狀態(tài)評估的步驟。本文將電力系統(tǒng)數(shù)據(jù)作為主要研究對象,闡述了電力系統(tǒng)數(shù)據(jù)質(zhì)量面臨的主要問題,參照國際標(biāo)準(zhǔn)ISO/IEC 25012,并從電力系統(tǒng)存在的質(zhì)量問題及電力大數(shù)據(jù)的特點(diǎn),首先提煉出電力系統(tǒng)數(shù)據(jù)質(zhì)量評估的一般指標(biāo),構(gòu)建電力大數(shù)據(jù)質(zhì)量評估的指標(biāo)體系,針對大數(shù)據(jù)處理的時(shí)效性問題,利用MapReduce并行化K-means聚類算法來實(shí)現(xiàn)大數(shù)據(jù)樣本集的快速預(yù)處理。然后利用熵權(quán)法計(jì)算指標(biāo)體系中的指標(biāo)各類數(shù)據(jù)集的客觀權(quán)重,最后采集某市電力公司所采集的用戶用電負(fù)荷數(shù)據(jù)進(jìn)行算例分析,采用灰色評估法判斷數(shù)據(jù)質(zhì)量所屬等級,在此基礎(chǔ)上實(shí)現(xiàn)對樣本數(shù)據(jù)集的綜合評價(jià)。計(jì)算結(jié)果表明,所提出的方法可以通過業(yè)務(wù)規(guī)則和需求對指標(biāo)體系有一個(gè)定量的描述,灰色熵權(quán)綜合評估法評價(jià)大數(shù)據(jù)質(zhì)量在時(shí)間效率上具有較好的表現(xiàn)。
[Abstract]:With the increase of the scale of power system, the development of measurement technology and the decrease of cost, the amount of data in power system is increasing rapidly and gradually has the characteristics of big data. Making full use of big data to improve power system planning, operation and control has been paid more and more attention. Therefore, how to evaluate the quality of big data is an important issue worth studying. Many researches have been reported on data quality improvement techniques such as data cleaning, data integration, similar record detection and so on. However, in the evaluation of data quality, the research work is still quite preliminary. Under this background, a comprehensive evaluation method of power big data quality is proposed for the characteristics of power system and power big data quality. For mass evaluation, sometimes the actual distribution of various kinds of quality is very different, so it is more reasonable to adopt the combined evaluation method, so the entropy weight method and the grey class evaluation method are combined together. The power big data quality assessment model is constructed on Hadoop platform, which lists the steps of power big data state evaluation in detail. In this paper, power system data is taken as the main research object, the main problems of power system data quality are expounded, referring to the international standard ISO/IEC 25012, and the quality problems of power system and the characteristics of power big data are discussed. Firstly, the general index of power system data quality evaluation is abstracted, and the index system of power big data quality evaluation is constructed. Aiming at the timeliness of big data processing, MapReduce parallel K-means clustering algorithm is used to realize the fast preprocessing of big data sample set. Then the objective weights of all kinds of data sets in the index system are calculated by using entropy weight method. Finally, the data collected by a power company in a certain city are analyzed by an example, and the grade of data quality is judged by grey evaluation method. On this basis, the comprehensive evaluation of the sample data set is realized. The calculation results show that the proposed method can describe the index system quantitatively through business rules and requirements, and the grey entropy weight comprehensive evaluation method has a good performance in evaluating the quality of big data in terms of time efficiency.
【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】：TM73

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