本文選題：并行框架(Spark) + 擾動(dòng)域��； 參考：《華北電力大學(xué)(北京)》2017年碩士論文

【摘要】：隨著電網(wǎng)互聯(lián)規(guī)模及耦合強(qiáng)度的日益擴(kuò)大,電網(wǎng)運(yùn)行環(huán)境日益復(fù)雜,亟需依托大數(shù)據(jù)技術(shù),提升電網(wǎng)多源大數(shù)據(jù)的挖掘深度及應(yīng)用效率。與此同時(shí),局部擾動(dòng)更易波及較大的區(qū)域,有效識(shí)別擾動(dòng)影響域?qū)τ谝种茢_動(dòng)傳播具有一定的工程應(yīng)用價(jià)值。針對(duì)大電網(wǎng)廣域時(shí)空序列數(shù)據(jù)的海量存儲(chǔ)、高效處理,提出以Spark為核心的電力大數(shù)據(jù)平臺(tái)設(shè)計(jì)框架。論文中指出了Spark在分布式計(jì)算中的優(yōu)勢(shì)及電力大數(shù)據(jù)平臺(tái)建設(shè)目標(biāo),并對(duì)平臺(tái)各個(gè)層次進(jìn)行詳細(xì)的論述,闡述了電網(wǎng)時(shí)空序列數(shù)據(jù)處理過(guò)程。在搭建的Spark和Hadoop實(shí)驗(yàn)環(huán)境基礎(chǔ)上,對(duì)典型聚類算法進(jìn)行性能對(duì)比測(cè)試,驗(yàn)證了Spark相對(duì)于Hadoop的Map Reduce計(jì)算模型數(shù)據(jù)處理的優(yōu)勢(shì)。從電網(wǎng)結(jié)構(gòu)脆弱性和運(yùn)行狀態(tài)脆弱性角度,提出勢(shì)能強(qiáng)度指標(biāo)衡量電網(wǎng)發(fā)生擾動(dòng)后各個(gè)節(jié)點(diǎn)受影響程度的強(qiáng)弱。通過(guò)變量相關(guān)性論述了單一變量識(shí)別擾動(dòng)影響域的約束性,并引入常用結(jié)構(gòu)脆弱性指標(biāo)電氣介數(shù)。基于能量函數(shù)構(gòu)造方法,構(gòu)建節(jié)點(diǎn)勢(shì)能函數(shù)方程,將電氣介數(shù)作為節(jié)點(diǎn)勢(shì)能(運(yùn)行狀態(tài)脆弱性)權(quán)重提出勢(shì)能強(qiáng)度指標(biāo)。通過(guò)對(duì)IEEE39節(jié)點(diǎn)系統(tǒng)不同故障對(duì)比仿真分析,得出高電氣介數(shù)節(jié)點(diǎn)更易成為勢(shì)能傳播路徑,驗(yàn)證了所提勢(shì)能強(qiáng)度指標(biāo)的正確性。以IEEE39節(jié)點(diǎn)系統(tǒng)仿真數(shù)據(jù)為數(shù)據(jù)源模擬流式數(shù)據(jù),基于Spark Streaming組件進(jìn)行在線擾動(dòng)影響域計(jì)算分析。根據(jù)基尼系數(shù)理論,提出勢(shì)能強(qiáng)度基尼系數(shù)方法,評(píng)估電網(wǎng)故障后網(wǎng)絡(luò)整體受擾動(dòng)情況。在線計(jì)算勢(shì)能強(qiáng)度值以及勢(shì)能強(qiáng)度基尼系數(shù)值,以勢(shì)能強(qiáng)度值作為聚類對(duì)象,采用流式K-Means聚類算法進(jìn)行擾動(dòng)影響域識(shí)別。在未采取任何抑制擾動(dòng)傳播措施情況下,動(dòng)態(tài)地分析了電網(wǎng)發(fā)生故障后擾動(dòng)影響域及勢(shì)能強(qiáng)度基尼系數(shù)的演變情況,綜合分析后指出擾動(dòng)傳播至一定程度后,總是趨于引起局部區(qū)域穩(wěn)定性下降。
[Abstract]:With the expansion of interconnection scale and coupling intensity, the operation environment of power grid is becoming more and more complex. It is urgent to improve the mining depth and application efficiency of multi-source big data based on big data technology. At the same time, the local disturbance is more easy to affect the larger region, and it has some engineering application value to effectively identify the disturbance influence region to suppress the disturbance propagation. Aiming at the mass storage and efficient processing of large area space-time sequence data in large power grid, a design framework of electric power big data platform based on Spark is proposed. This paper points out the advantages of Spark in distributed computing and the construction goal of electric power big data platform, and discusses in detail all levels of the platform, and expounds the process of data processing in time and space series of power grid. Based on the experimental environment of Spark and Hadoop, the performance of the typical clustering algorithm is compared and tested, and the advantages of Spark compared with the Map Reduce computing model of Hadoop are verified. From the point of view of structural fragility and operational state vulnerability, a potential energy intensity index is proposed to measure the degree of influence of each node after disturbance. In this paper, the constraint of single variable identification of disturbance influence region is discussed by variable correlation, and the electrical quotient of structural vulnerability index is introduced. Based on the energy function construction method, the node potential energy function equation is constructed, and the electrical medium is taken as the weight of the node potential energy (running state fragility) to put forward the potential energy intensity index. By comparing and analyzing the different faults of the IEEE39 node system, it is concluded that the high electrical intermediate node is more likely to become the potential energy transmission path, and the correctness of the proposed potential energy intensity index is verified. The IEEE39 node system simulation data is used as the data source to simulate the flow data, and the on-line disturbance influence domain is calculated and analyzed based on the Spark Streaming component. According to the Gini coefficient theory, a Gini coefficient method of potential energy intensity is proposed to evaluate the disturbance of the whole network after power network failure. The potential energy intensity and the Gini coefficient of potential energy intensity are calculated on line, and the potential energy intensity is used as the clustering object. Flow K-Means clustering algorithm is used to identify the disturbance influence domain. In the absence of any measures to restrain disturbance propagation, the evolution of disturbance influence region and Gini coefficient of potential energy intensity after power network failure are dynamically analyzed. After comprehensive analysis, it is pointed out that the disturbance propagates to a certain extent. The stability of local region tends to decrease.
【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
【分類號(hào)】：TM712;TM732

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