本文選題：智能電網(wǎng)調(diào)度　切入點(diǎn)：云計(jì)算　出處：《華北電力大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：電網(wǎng)調(diào)度系統(tǒng)是整個(gè)電力系統(tǒng)的中樞。我國電網(wǎng)互聯(lián)程度的不斷提高以及智能電網(wǎng)建設(shè)過程的逐步推進(jìn)，給現(xiàn)有的電網(wǎng)調(diào)度系統(tǒng)帶來了極大的挑戰(zhàn)。其主要挑戰(zhàn)就來自于現(xiàn)有的電力計(jì)算平臺(tái)，其計(jì)算與存儲(chǔ)能力已經(jīng)無法滿足電網(wǎng)發(fā)展的需求，，考慮到電網(wǎng)發(fā)展的計(jì)算平臺(tái)計(jì)算及存儲(chǔ)能力不斷增長的趨勢(shì)，建設(shè)具有海量數(shù)據(jù)計(jì)算和存儲(chǔ)的智能電網(wǎng)調(diào)度系統(tǒng)已迫在眉睫。 首先，本文在結(jié)合我國現(xiàn)有電網(wǎng)調(diào)度體系的基礎(chǔ)上，提出了基于云計(jì)算的智能電網(wǎng)調(diào)度系統(tǒng)新架構(gòu)以及云計(jì)算數(shù)據(jù)中心的體系結(jié)構(gòu)，從宏觀上對(duì)整個(gè)智能電網(wǎng)調(diào)度體系結(jié)構(gòu)進(jìn)行了規(guī)劃。 其次，本文針對(duì)智能電網(wǎng)調(diào)度云數(shù)據(jù)中心的三個(gè)關(guān)鍵問題進(jìn)行了重點(diǎn)研究。第一、云數(shù)據(jù)中心內(nèi)部網(wǎng)絡(luò)結(jié)構(gòu)。在分析對(duì)比傳統(tǒng)網(wǎng)絡(luò)結(jié)構(gòu)算法以及最新提出的BCube、DCell數(shù)據(jù)中心網(wǎng)絡(luò)結(jié)構(gòu)算法的前提下，提出了更具有擴(kuò)展性、容錯(cuò)性的PCube網(wǎng)絡(luò)結(jié)構(gòu)算法；第二、Hadoop云平臺(tái)的副本存儲(chǔ)策略。針對(duì)Hadoop現(xiàn)有副本存放策略的不足，本文研究目前學(xué)者提出HDFS改進(jìn)存儲(chǔ)策略——CoHadoop副本存儲(chǔ)策略，并在此基礎(chǔ)上對(duì)其智能性加以改進(jìn)，依靠FP數(shù)組挖掘算法對(duì)Locator的相關(guān)性進(jìn)行定義，完善了CoHadoop算法的性能，使其更好的適合智能電網(wǎng)調(diào)度系統(tǒng)中事務(wù)數(shù)據(jù)集大而稀疏的特點(diǎn)；第三、Hadoop云平臺(tái)的任務(wù)調(diào)度策略。本文改進(jìn)了現(xiàn)有Hadoop平臺(tái)的公平調(diào)度算法，基于模擬退火策略對(duì)任務(wù)選取進(jìn)行優(yōu)化以適合智能電網(wǎng)調(diào)度系統(tǒng)任務(wù)多、優(yōu)先級(jí)區(qū)分明顯等特點(diǎn)。 最后，為驗(yàn)證相關(guān)算法的有效性，隨著研究過程的進(jìn)行，本文分別對(duì)網(wǎng)絡(luò)結(jié)構(gòu)算法進(jìn)行模擬實(shí)驗(yàn)，驗(yàn)證了其擴(kuò)展性、容錯(cuò)性等關(guān)鍵特性；之后為驗(yàn)證Hadoop副本存儲(chǔ)策略和任務(wù)調(diào)度算法的性能，本文借助一個(gè)已有云計(jì)算平臺(tái)搭建了Hadoop集群，對(duì)相關(guān)實(shí)驗(yàn)環(huán)境進(jìn)行了模擬并運(yùn)行了相關(guān)算法實(shí)例，驗(yàn)證了算法的有效性。
[Abstract]:The power grid dispatching system is the center of the whole power system. The main challenge comes from the existing electric power computing platform, whose computing and storage capacity can no longer meet the needs of the development of the power grid. Considering the increasing computing and storage capacity of power grid, it is urgent to build a smart grid dispatching system with massive data computing and storage. First of all, this paper proposes a new architecture of smart grid dispatching system based on cloud computing and the architecture of cloud computing data center based on the existing power grid dispatching system in China. The whole dispatching architecture of smart grid is planned macroscopically. Secondly, this paper focuses on three key issues of smart grid dispatching cloud data center. On the premise of analyzing the network structure of cloud data center, a more extensible and fault-tolerant PCube network structure algorithm is proposed, which is more scalable and more fault-tolerant than the traditional network structure algorithm and the latest BCube DCell data center network structure algorithm. Second, the replica storage strategy of Hadoop cloud platform. Aiming at the deficiency of the existing replica storage strategy of Hadoop, this paper proposes an improved storage strategy for HDFS, which is CoHadoop replica storage strategy, and then improves its intelligence. The correlation of Locator is defined by FP array mining algorithm, which improves the performance of CoHadoop algorithm and makes it more suitable for the characteristics of large and sparse transaction data set in smart grid dispatching system. The third is the task scheduling strategy of Hadoop cloud platform. This paper improves the fair scheduling algorithm of the existing Hadoop platform and optimizes the task selection based on simulated annealing strategy to suit for the smart grid scheduling system with many tasks and distinct priorities. Finally, in order to verify the effectiveness of the algorithm, with the progress of the research process, the network structure algorithm is simulated to verify its scalability, fault tolerance and other key characteristics. Then in order to verify the performance of Hadoop replica storage strategy and task scheduling algorithm this paper builds a Hadoop cluster with the help of an existing cloud computing platform and simulates the related experimental environment and runs an example of relevant algorithms to verify the effectiveness of the algorithm.
【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】：2013
【分類號(hào)】：TP308;TP311.13

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