【摘要】：智能電網(wǎng)狀態(tài)監(jiān)測通過分析電網(wǎng)狀態(tài)數(shù)據(jù),可以實時監(jiān)控和預測電力系統(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)需要實時性處理,這就需要利用云存儲技術對海量的電網(wǎng)監(jiān)測數(shù)據(jù)進行快速有效地處理與存儲。 本文利用云計算中的MapReduce并行數(shù)據(jù)處理編程模型、BigTable和GFS數(shù)據(jù)存儲技術,提出了智能電網(wǎng)監(jiān)測數(shù)據(jù)的云存儲原型系統(tǒng),詳細介紹了云存儲系統(tǒng)整體設計、云存儲構架,同時提出了云存儲系統(tǒng)的運行流程和云存儲構架的故障恢復策略,構建了一個完整、高效、可靠地數(shù)據(jù)存儲和處理的系統(tǒng)。 結合聚類算法和一致Hash算法設計了數(shù)據(jù)均衡分布算法,進行數(shù)據(jù)分布。首先,綜合處理器、內(nèi)存、網(wǎng)速等因素,進行存儲設備聚類,并優(yōu)先使用性能高的數(shù)據(jù)服務器；其次,在每個聚類設備內(nèi)部,利用一致Hash算法均衡地將數(shù)據(jù)分布在聚類內(nèi)部的各個服務器上。 為了進一步滿足數(shù)據(jù)之間的關聯(lián)性、數(shù)據(jù)的訪問便利性,尋找高效地進行計算遷移方式的網(wǎng)絡環(huán)境,需要對已經(jīng)存儲的數(shù)據(jù)進行數(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)過實驗證明,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)化對查詢語句進行優(yōu)化,提高查詢效率。進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器協(xié)同查詢步驟：迭代查詢和遞歸查詢,并做了對比。 本文的涉及范圍從智能電網(wǎng)監(jiān)測數(shù)據(jù)的云存儲原型系統(tǒng),到數(shù)據(jù)均衡分布和優(yōu)化再分布,到分布數(shù)據(jù)的多服務器的分布式協(xié)同數(shù)據(jù)查詢,整個從數(shù)據(jù)存儲到數(shù)據(jù)查詢,形成一個完整的體系。
[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.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TP333;TM769

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本文編號：2144262