【摘要】：電網(wǎng)時(shí)序數(shù)據(jù)是電網(wǎng)設(shè)備狀態(tài)監(jiān)測(cè)、故障診斷的重要基礎(chǔ),對(duì)實(shí)時(shí)性要求較高。然而,現(xiàn)有的數(shù)據(jù)質(zhì)量治理方法大多側(cè)重于數(shù)據(jù)庫(kù)中已有的歷史數(shù)據(jù),難以滿足實(shí)時(shí)性的要求,且所采用的方法以及框架由于自身存在的問(wèn)題,難以應(yīng)對(duì)超大規(guī)模的數(shù)據(jù)集。本文以“面向電網(wǎng)時(shí)序數(shù)據(jù)的數(shù)據(jù)質(zhì)量實(shí)時(shí)治理技術(shù)研究”為課題,旨在研究分布式實(shí)時(shí)計(jì)算系統(tǒng)Storm,并將其與時(shí)間序列序列分析、數(shù)據(jù)清洗技術(shù)相結(jié)合,解決大規(guī)模數(shù)據(jù)集實(shí)時(shí)治理的問(wèn)題。本文首先深入研究時(shí)間序列分析技術(shù)的原理與方法,對(duì)時(shí)間序列預(yù)測(cè)模型ARIMA與智能電網(wǎng)時(shí)序數(shù)據(jù)的特點(diǎn)進(jìn)行分析;其次,對(duì)數(shù)據(jù)質(zhì)量控制的方法進(jìn)行歸納總結(jié);最后設(shè)計(jì)了面向電網(wǎng)時(shí)序數(shù)據(jù)的數(shù)據(jù)質(zhì)量實(shí)時(shí)治理框架以及適用于該框架的時(shí)序數(shù)據(jù)存儲(chǔ)模式。本文利用所提出的框架開(kāi)展針對(duì)海量時(shí)序數(shù)據(jù)源的實(shí)時(shí)并發(fā)治理,對(duì)時(shí)序數(shù)據(jù)進(jìn)行預(yù)測(cè),比較不同數(shù)據(jù)樣本對(duì)預(yù)測(cè)值的影響,分別采用基于統(tǒng)計(jì)與基于聚類(lèi)的方法,實(shí)時(shí)識(shí)別數(shù)據(jù)中的孤立點(diǎn),為電網(wǎng)當(dāng)前運(yùn)行狀態(tài)診斷與未來(lái)發(fā)展趨勢(shì)挖掘提供支撐平臺(tái)。實(shí)例從預(yù)測(cè)精度、運(yùn)算速度、占用資源等角度驗(yàn)證了本框架的有效性與實(shí)用性。
[Abstract]:Power system timing data is an important basis for power equipment condition monitoring and fault diagnosis, and requires high real-time performance. However, most of the existing data quality governance methods focus on the existing historical data in the database, which is difficult to meet the real-time requirements, and the method and framework can not cope with the large scale data set because of its own problems. In this paper, we focus on the research of data quality real-time governance technology for power grid time series data. The purpose of this paper is to study the distributed real-time computing system, and combine it with time series analysis and data cleaning technology. To solve the problem of real-time governance of large-scale data sets. In this paper, the principle and method of time series analysis are studied, and the characteristics of time series prediction model (ARIMA) and smart grid time series data are analyzed, secondly, the methods of data quality control are summarized. Finally, a real-time data quality governance framework for power system timing data is designed. In this paper, we use the proposed framework to implement real-time concurrency governance for massive time series data sources, predict the time series data, compare the effects of different data samples on the predicted values, and adopt statistical and clustering based methods, respectively. Real-time identification of outliers in data provides a supporting platform for current power grid state diagnosis and future trend mining. The effectiveness and practicability of the framework are verified by examples from the angles of prediction accuracy, operation speed and resource occupation.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TM507

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本文編號(hào)：2150639