智能電網(wǎng)監(jiān)測(cè)數(shù)據(jù)的云存儲(chǔ)研究
發(fā)布時(shí)間：2018-07-25 15:34
【摘要】：智能電網(wǎng)狀態(tài)監(jiān)測(cè)通過分析電網(wǎng)狀態(tài)數(shù)據(jù),可以實(shí)時(shí)監(jiān)控和預(yù)測(cè)電力系統(tǒng)狀況。電網(wǎng)系統(tǒng)中的狀態(tài)數(shù)據(jù)數(shù)量巨大,格式多樣、不統(tǒng)一,有的數(shù)據(jù)需要實(shí)時(shí)性處理,這就需要利用云存儲(chǔ)技術(shù)對(duì)海量的電網(wǎng)監(jiān)測(cè)數(shù)據(jù)進(jìn)行快速有效地處理與存儲(chǔ)。 本文利用云計(jì)算中的MapReduce并行數(shù)據(jù)處理編程模型、BigTable和GFS數(shù)據(jù)存儲(chǔ)技術(shù),提出了智能電網(wǎng)監(jiān)測(cè)數(shù)據(jù)的云存儲(chǔ)原型系統(tǒng),詳細(xì)介紹了云存儲(chǔ)系統(tǒng)整體設(shè)計(jì)、云存儲(chǔ)構(gòu)架,同時(shí)提出了云存儲(chǔ)系統(tǒng)的運(yùn)行流程和云存儲(chǔ)構(gòu)架的故障恢復(fù)策略,構(gòu)建了一個(gè)完整、高效、可靠地?cái)?shù)據(jù)存儲(chǔ)和處理的系統(tǒng)。 結(jié)合聚類算法和一致Hash算法設(shè)計(jì)了數(shù)據(jù)均衡分布算法,進(jìn)行數(shù)據(jù)分布。首先,綜合處理器、內(nèi)存、網(wǎng)速等因素,進(jìn)行存儲(chǔ)設(shè)備聚類,并優(yōu)先使用性能高的數(shù)據(jù)服務(wù)器；其次,在每個(gè)聚類設(shè)備內(nèi)部,利用一致Hash算法均衡地將數(shù)據(jù)分布在聚類內(nèi)部的各個(gè)服務(wù)器上。 為了進(jìn)一步滿足數(shù)據(jù)之間的關(guān)聯(lián)性、數(shù)據(jù)的訪問便利性,尋找高效地進(jìn)行計(jì)算遷移方式的網(wǎng)絡(luò)環(huán)境,需要對(duì)已經(jīng)存儲(chǔ)的數(shù)據(jù)進(jìn)行數(shù)據(jù)分布的再優(yōu)化。本文利用遺傳算法,選擇出最合理的數(shù)據(jù)分布的優(yōu)化方法。經(jīng)過實(shí)驗(yàn)證明,本文提出的數(shù)據(jù)分布算法具有可行性。 數(shù)據(jù)查詢由于查詢的順序不同而造成查詢效率的天壤之別,再加上分布式數(shù)據(jù)的特殊查詢流程,使得數(shù)據(jù)查詢效率差距更大。本文比較了不同查詢方法,顯示了不同查詢方法的查詢效率的差別。利用代數(shù)優(yōu)化對(duì)查詢語(yǔ)句進(jìn)行優(yōu)化,提高查詢效率。進(jìn)而又證明了分布式數(shù)據(jù)查詢方法的可行性。最后給出了兩種多服務(wù)器協(xié)同查詢步驟：迭代查詢和遞歸查詢,并做了對(duì)比。 本文的涉及范圍從智能電網(wǎng)監(jiān)測(cè)數(shù)據(jù)的云存儲(chǔ)原型系統(tǒng),到數(shù)據(jù)均衡分布和優(yōu)化再分布,到分布數(shù)據(jù)的多服務(wù)器的分布式協(xié)同數(shù)據(jù)查詢,整個(gè)從數(shù)據(jù)存儲(chǔ)到數(shù)據(jù)查詢,形成一個(gè)完整的體系。
[Abstract]:State monitoring of smart grid can monitor and predict the state of power system in real time by analyzing the state data of power system. The state data in the power system is large in quantity, diverse in format and not uniform. Some of the data need real-time processing, which requires the use of cloud storage technology to quickly and effectively process and store the massive power grid monitoring data. In this paper, using the MapReduce parallel data processing programming model in cloud computing, BigTable and GFS data storage technology, a cloud storage prototype system for smart grid monitoring data is proposed, and the whole design of cloud storage system and cloud storage architecture are introduced in detail. At the same time, the operation flow of cloud storage system and the fault recovery strategy of cloud storage architecture are proposed, and a complete, efficient and reliable data storage and processing system is constructed. Combined with clustering algorithm and uniform Hash algorithm, the data equilibrium distribution algorithm is designed to carry out data distribution. First of all, integrate processor, memory, network speed and other factors to cluster storage devices, and give priority to the use of high-performance data servers; second, within each cluster device, The uniform Hash algorithm is used to distribute the data evenly among the servers within the cluster. In order to further satisfy the relationship between data, the convenience of data access, and to find a network environment that can efficiently compute and migrate, it is necessary to optimize the data distribution of the stored data. In this paper, genetic algorithm is used to select the most reasonable data distribution optimization method. Experimental results show that the proposed data distribution algorithm is feasible. Because the order of data query is different, the query efficiency is greatly different, and the special query flow of distributed data makes the difference of data query efficiency even bigger. This paper compares different query methods and shows the difference of query efficiency of different query methods. The query statements are optimized by algebraic optimization to improve the query efficiency. Furthermore, the feasibility of distributed data query method is proved. Finally, two kinds of multi-server cooperative query steps, iterative query and recursive query, are given and compared. The scope of this paper ranges from cloud storage prototype system of smart grid monitoring data, to data balanced distribution and optimal redistribution, to distributed collaborative data query of multiple servers, from data storage to data query. Form a complete system.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP333;TM769
本文編號(hào)：2144262
[Abstract]:State monitoring of smart grid can monitor and predict the state of power system in real time by analyzing the state data of power system. The state data in the power system is large in quantity, diverse in format and not uniform. Some of the data need real-time processing, which requires the use of cloud storage technology to quickly and effectively process and store the massive power grid monitoring data. In this paper, using the MapReduce parallel data processing programming model in cloud computing, BigTable and GFS data storage technology, a cloud storage prototype system for smart grid monitoring data is proposed, and the whole design of cloud storage system and cloud storage architecture are introduced in detail. At the same time, the operation flow of cloud storage system and the fault recovery strategy of cloud storage architecture are proposed, and a complete, efficient and reliable data storage and processing system is constructed. Combined with clustering algorithm and uniform Hash algorithm, the data equilibrium distribution algorithm is designed to carry out data distribution. First of all, integrate processor, memory, network speed and other factors to cluster storage devices, and give priority to the use of high-performance data servers; second, within each cluster device, The uniform Hash algorithm is used to distribute the data evenly among the servers within the cluster. In order to further satisfy the relationship between data, the convenience of data access, and to find a network environment that can efficiently compute and migrate, it is necessary to optimize the data distribution of the stored data. In this paper, genetic algorithm is used to select the most reasonable data distribution optimization method. Experimental results show that the proposed data distribution algorithm is feasible. Because the order of data query is different, the query efficiency is greatly different, and the special query flow of distributed data makes the difference of data query efficiency even bigger. This paper compares different query methods and shows the difference of query efficiency of different query methods. The query statements are optimized by algebraic optimization to improve the query efficiency. Furthermore, the feasibility of distributed data query method is proved. Finally, two kinds of multi-server cooperative query steps, iterative query and recursive query, are given and compared. The scope of this paper ranges from cloud storage prototype system of smart grid monitoring data, to data balanced distribution and optimal redistribution, to distributed collaborative data query of multiple servers, from data storage to data query. Form a complete system.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP333;TM769
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 劉仲,周興銘;基于動(dòng)態(tài)區(qū)間映射的數(shù)據(jù)對(duì)象布局算法[J];軟件學(xué)報(bào);2005年11期
2 陳玉林;陳允平;孫金莉;邱君瑪;;電網(wǎng)故障診斷方法綜述[J];中國(guó)電力;2006年05期
，本文編號(hào)：2144262
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