發(fā)布時間：2018-03-13 14:55

  本文選題：電力調(diào)度控制系統(tǒng)　切入點：多源異構(gòu)數(shù)據(jù)　出處：《華北電力大學(xué)(北京)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著智能電網(wǎng)建設(shè)的深入實施和智能傳感設(shè)備的廣泛使用,電力系統(tǒng)的數(shù)據(jù)量呈爆炸性增長趨勢,電力行業(yè)迎來了大數(shù)據(jù)時代。電力大數(shù)據(jù)雖然擁有廣闊的應(yīng)用前景,但是面臨著數(shù)據(jù)量大、數(shù)據(jù)異構(gòu)和數(shù)據(jù)分散等問題。在電力調(diào)度控制系統(tǒng)中,為了實現(xiàn)調(diào)控大數(shù)據(jù)的有效利用,需要通過多源異構(gòu)數(shù)據(jù)處理方法將調(diào)控多源異構(gòu)數(shù)據(jù)進行信息共享,并建立統(tǒng)一的數(shù)據(jù)模型和全景調(diào)控數(shù)據(jù),其對調(diào)控多源異構(gòu)數(shù)據(jù)處理和數(shù)據(jù)融合技術(shù)的研究具有重要的意義。首先,本文在分析電力調(diào)度控制系統(tǒng)數(shù)據(jù)特點、需求及多源異構(gòu)數(shù)據(jù)預(yù)處理方法的基礎(chǔ)上,提出了全景調(diào)控統(tǒng)一數(shù)據(jù)模型和調(diào)控多源異構(gòu)數(shù)據(jù)ETL處理模型來滿足建立調(diào)控全景數(shù)據(jù)的需求,并對模型的體系框架和工作流程進行了分析。其次,針對構(gòu)建調(diào)控全景數(shù)據(jù)過程中出現(xiàn)的不完整數(shù)據(jù),本文提出了一種面向統(tǒng)一數(shù)據(jù)模型的缺失數(shù)據(jù)填補算法。該算法采用改進的混沌遺傳優(yōu)化方法估計不完整數(shù)據(jù)的均值和協(xié)方差對應(yīng)的最佳參數(shù),再根據(jù)已知數(shù)據(jù)利用改進馬爾可夫蒙特卡洛方法估計缺失數(shù)據(jù),解決了調(diào)控數(shù)據(jù)中的缺失問題。結(jié)果表明,該算法能通過較少的迭代次數(shù)獲得不完整調(diào)控數(shù)據(jù)的最佳參數(shù),同時,缺失數(shù)據(jù)的估計值更加準確,有效的保證了數(shù)據(jù)的準確性和完整性。最后,針對調(diào)控多源異構(gòu)數(shù)據(jù)融合中存在的字符串匹配問題,本文提出了一種面向電力調(diào)控系統(tǒng)數(shù)據(jù)的字符串匹配算法。為了準確快速的匹配字符串,該算法依據(jù)電力調(diào)度控制系統(tǒng)數(shù)據(jù)特點制定了匹配規(guī)則,同時提出了一種匹配度計算方法,該方法將字符串的相似程度合理量化,促進調(diào)控字符串數(shù)據(jù)匹配正確率的提高。通過實驗仿真分析,驗證了該算法有助于提高調(diào)控系統(tǒng)字符串數(shù)據(jù)匹配的正確率,促進了調(diào)控多源異構(gòu)數(shù)據(jù)的融合。
[Abstract]:With the deep implementation of smart grid construction and the extensive use of intelligent sensing equipment, the data volume of power system is increasing explosively, and the power industry has ushered in big data's time. However, in order to realize the effective use of big data in the electric power dispatching control system, there are many problems, such as large amount of data, heterogeneous data and scattered data, etc. It is necessary to share the information of multi-source and heterogeneous data through multi-source and heterogeneous data processing method, and establish a unified data model and panoramic control data. It is of great significance to study the technology of regulating multi-source heterogeneous data processing and data fusion. Firstly, based on the analysis of data characteristics, requirements and preprocessing methods of multi-source heterogeneous data, this paper analyzes the data characteristics, requirements and methods of multi-source heterogeneous data processing in power dispatching control system. The unified data model of panoramic control and the ETL processing model of multi-source heterogeneous data are put forward to meet the requirements of establishing the panoramic data, and the architecture and workflow of the model are analyzed. For the incomplete data that appears in the process of building the control panoramic data, In this paper, a missing data filling algorithm for uniform data model is proposed, which uses an improved chaotic genetic optimization method to estimate the optimal parameters corresponding to the mean and covariance of incomplete data. Based on the known data, the missing data is estimated by the improved Markov Monte Carlo method, and the problem of missing data is solved. The results show that the optimal parameters of incomplete control data can be obtained by the algorithm with fewer iterations. At the same time, the estimation of missing data is more accurate, which effectively ensures the accuracy and integrity of the data. Finally, aiming at the string matching problem in multi-source heterogeneous data fusion, In this paper, a string matching algorithm for power regulation and control system data is proposed. In order to match string accurately and quickly, the algorithm formulates matching rules according to the data characteristics of power dispatching control system. At the same time, a method of calculating matching degree is proposed, which quantifies the similarity of string reasonably, and promotes the improvement of matching accuracy of string data. It is verified that this algorithm can improve the accuracy of string data matching and promote the fusion of multi-source heterogeneous data.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM73

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