本文選題：大數(shù)據(jù)分析　切入點：輸電線路　出處：《湖北工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著現(xiàn)代輸電網(wǎng)的規(guī)模、容量和覆蓋范圍越來越大,輸電線路在國民經(jīng)濟(jì)中占的地位越來越重要,電網(wǎng)停電故障將會給社會生產(chǎn)和人民生活,造成重大的經(jīng)濟(jì)損失。源于輸電線路具有運(yùn)送距離長、覆蓋區(qū)域廣等特點,容易受天然環(huán)境及人為成分的影響較多,致使線路的運(yùn)作維護(hù)工作存在較大的困難。怎樣有效提高輸電線路的運(yùn)行和維護(hù)的質(zhì)量,以確保電網(wǎng)穩(wěn)定安全運(yùn)行,現(xiàn)已成為廣大研究人員及電力部門努力探討的課題。。近幾年來,當(dāng)代信息社會已經(jīng)進(jìn)入了大數(shù)據(jù)時期,大數(shù)據(jù)火速發(fā)展成長為深受學(xué)術(shù)界和產(chǎn)業(yè)界眷顧的熱門范疇,并得到了大量的運(yùn)用。大數(shù)據(jù)分析方式可以從輸電系統(tǒng)海量的信息中,找出潛藏的模式和其中的規(guī)則,為維護(hù)職員供給相關(guān)維護(hù)策略支持。基于大數(shù)據(jù)算法的輸電線路故障分析作為調(diào)度人員處理事件的協(xié)助工具,能夠起到降低事故處理的耗時,防止事故擴(kuò)張的作用。首先,本文分析了輸電線路的常見故障,大數(shù)據(jù)分析方法的應(yīng)用領(lǐng)域,及目前輸電線路智能化診斷中運(yùn)用的大數(shù)據(jù)算法和其不足。為利用大數(shù)據(jù)算法解決輸電線路故障分析問題,提供了豐富的理論依據(jù)和新的思路。其次,在現(xiàn)有研究成果的基礎(chǔ)上,本文設(shè)計了輸電線路故障分析模型,根據(jù)k最近鄰分類算法(k-Nearest Neighbor,KNN)的特點,結(jié)合模糊理論,提出了模糊KNN算法模型,解決了KNN在處理類別界限不明顯數(shù)據(jù)時的問題。實驗結(jié)果表明,模糊KNN算法模型提升了KNN故障類型診斷分析的準(zhǔn)確率。同時,本文基于Spark計算平臺,開發(fā)了輸電線路實時故障分析模型,滿足了電網(wǎng)實時故障分析的需求。然后,本文針對模糊KNN算法對于混合故障大數(shù)據(jù)類別分析準(zhǔn)確率低的問題,對輸電線路故障分析模型進(jìn)行優(yōu)化,研發(fā)了基于密度邏輯回歸的多分類模型(Multi-Classification based on Density logistic regression,MCDLR),大大提升了現(xiàn)有輸電線路故障數(shù)據(jù)分類方法的準(zhǔn)確性。最后,本文進(jìn)行了大量的輸電線路故障分析實驗,充分證實了文中提出的算法模型的可行性和有效性。本文提出的算法模型不僅能夠滿足輸電線路故障分析的需求,而且實現(xiàn)了大數(shù)據(jù)分析算法在電力系統(tǒng)的應(yīng)用研究,具有良好的創(chuàng)新性和實踐可行性。
[Abstract]:With the scale, capacity and coverage of modern transmission network increasing, transmission lines play a more and more important role in the national economy. Power outages will bring social production and life to the people. Because transmission lines are characterized by long transportation distance and wide coverage, they are vulnerable to the influence of natural environment and human composition. The operation and maintenance of transmission line is difficult. How to improve the quality of operation and maintenance of transmission line effectively in order to ensure the stable and safe operation of power network, In recent years, the contemporary information society has entered the period of big data, and big data has rapidly developed into a hot category deeply favored by academia and industry. Big data can find hidden patterns and rules from the vast amount of information in the transmission system. Transmission line fault analysis based on big data algorithm, as a tool to assist dispatchers in handling incidents, can reduce the time consuming and prevent the expansion of accidents. In this paper, the common faults of transmission line, the application field of big data's analysis method, the big data algorithm used in intelligent diagnosis of transmission line and its shortcomings are analyzed. It provides abundant theoretical basis and new ideas. Secondly, based on the existing research results, this paper designs a fault analysis model for transmission lines, combining with fuzzy theory, according to the characteristics of k-nearest neighbor classification algorithm (KNN). A fuzzy KNN algorithm model is proposed, which solves the problem of KNN in dealing with unobvious data of class boundaries. The experimental results show that the fuzzy KNN algorithm model improves the accuracy of fault type diagnosis and analysis of KNN. At the same time, based on the Spark computing platform, this paper proposes a fuzzy KNN algorithm model. The real-time fault analysis model of transmission line is developed to meet the need of real-time fault analysis of power network. Secondly, the fuzzy KNN algorithm is used to analyze the big data class of hybrid faults with low accuracy. Based on the optimization of transmission line fault analysis model, a multi-classification based on Density logistic regression model based on density logic regression is developed, which greatly improves the accuracy of existing fault data classification methods for transmission lines. In this paper, a large number of transmission line fault analysis experiments have been carried out, which fully verify the feasibility and effectiveness of the proposed algorithm model, which can not only meet the needs of transmission line fault analysis. Moreover, the application of big data analysis algorithm in power system is realized, which has good innovation and practical feasibility.
【學(xué)位授予單位】：湖北工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.13;TM755

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