本文選題：大用戶 + 用電行為��； 參考：《華北電力大學》2017年碩士論文

【摘要】：目前,隨著國民經(jīng)濟的發(fā)展以及產(chǎn)業(yè)結(jié)構(gòu)的調(diào)整,國家電網(wǎng)設(shè)備已經(jīng)向大容量、高參數(shù)自控設(shè)備升級,導(dǎo)致了大用戶即電壓等級高、負荷大的用戶數(shù)量明顯增加。也就是說智能電網(wǎng)的要求越來越多,電力系統(tǒng)負荷越來越大,調(diào)度管理的作用越來越重要。再加上信息采集系統(tǒng)應(yīng)用的擴展,用戶用電負荷數(shù)據(jù)成海量態(tài)勢增長。因此,對電網(wǎng)企業(yè)的電力信息化建設(shè)提出了更高的要求。如何處理不斷增長的大用戶用電負荷數(shù)據(jù),進行快速有效地用電行為分析成為了重要課題。在此基礎(chǔ)上,本文對如下問題開展了研究。首先,根據(jù)大用戶用電負荷數(shù)據(jù)特點,選擇模糊聚類算法進行用電負荷特性分析。為了解決傳統(tǒng)FCM算法聚類效果一般、易陷入局部解的問題,本文利用免疫雙態(tài)粒子群算法來改進FCM算法,設(shè)計出免疫雙態(tài)粒子群模糊C均值聚類算法,該算法在全局收斂能力方面具有優(yōu)勢。其次,考慮到任務(wù)選擇資源的不確定性對任務(wù)執(zhí)行速度的影響,采用Min-Min啟發(fā)式算法和吞吐量驅(qū)動的調(diào)度機制,依據(jù)任務(wù)的偏好類型,設(shè)計出吞吐量驅(qū)動最小代價模糊C均值聚類算法,該算法可以提高系統(tǒng)資源利用率和吞吐能力,保證系統(tǒng)負載均衡性。最后,結(jié)合這兩種算法的優(yōu)點,給出一種新的吞吐量驅(qū)動最小代價免疫雙態(tài)粒子群模糊C均值聚類算法,并運用了Spark內(nèi)存批處理技術(shù),使該算法可以在云平臺上并行執(zhí)行,從而解決日益增長的大用戶用電數(shù)據(jù)量與算法執(zhí)行性能相矛盾的問題。為了驗證本文設(shè)計的算法可以有效地分析用戶用電行為,并且對任務(wù)調(diào)度有良好的優(yōu)化效果,在實驗室搭建的云集群上運行設(shè)計的算法進行驗證。
[Abstract]:At present, with the development of the national economy and the adjustment of the industrial structure, the equipment of the State Grid has been upgraded to large capacity and high parameter automatic control equipment, which has led to the increase of the number of the large users, that is, the high voltage grade and the heavy load. In other words, the demand of smart grid is more and more, the load of power system is increasing, and the role of dispatching management is becoming more and more important. In addition, with the expansion of the application of information collection system, the power load data of users is growing in a huge amount. Therefore, higher requirements are put forward for electric power informatization construction of power grid enterprises. How to deal with the increasing load data of large users and how to analyze the power consumption behavior quickly and effectively has become an important subject. On this basis, this paper studies the following issues. Firstly, according to the characteristics of large user load data, fuzzy clustering algorithm is selected to analyze the power load characteristics. In order to solve the problem that the clustering effect of traditional FCM algorithm is general and easy to fall into local solution, the immune double state particle swarm optimization algorithm is used to improve the FCM algorithm and the immune double state particle swarm fuzzy C-means clustering algorithm is designed. The algorithm has advantages in global convergence ability. Secondly, considering the effect of uncertainty of task selection resources on task execution speed, Min-Min heuristic algorithm and throughput driven scheduling mechanism are used according to task preference type. A throughput driven minimum cost fuzzy C-means clustering algorithm is designed, which can improve system resource utilization and throughput capacity and ensure system load balance. Finally, combining the advantages of these two algorithms, a new throughput driven immune two-state particle swarm fuzzy C-means clustering algorithm is proposed, and Spark memory batch technology is used to make the algorithm run in parallel on the cloud platform. In order to solve the problem that the increasing amount of power consumption of large users contradicts the performance of the algorithm. In order to verify that the algorithm designed in this paper can effectively analyze the power consumption behavior of users and has a good optimization effect on task scheduling, the designed algorithm is run on the cloud cluster built in the laboratory to verify the proposed algorithm.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM73

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本文編號：2073908