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  本文關鍵詞：油浸式變壓器故障率模型及故障診斷研究　出處：《浙江大學》2014年碩士論文　論文類型：學位論文

  更多相關文章： 油浸式變壓器 油中溶解氣體分析 比例故障率模型 支持向量機 故障診斷 故障類別概率

【摘要】：電力變壓器是電網(wǎng)中的核心部件,也是電網(wǎng)公司的重要資產,其安全穩(wěn)定運行意義重大。在實時監(jiān)測變壓器狀態(tài)、運行條件的基礎上,可對變壓器的故障率進行評價,進而合理安排檢修維護措施來降低設備發(fā)生故障的風險。此外,對已經(jīng)故障停運或故障率高的變壓器進行故障診斷,迅速找到故障原因并采取相應修復措施,能夠有效減少維護時間,降低變壓器停運造成的經(jīng)濟損失。因此,本文針對油浸變壓器的故障率建模方法和故障診斷方法,開展了以下研究。 基于比例故障率模型(Proportional Hazard Model, PHM)和油中溶解氣體信息提出了一種綜合考慮老化和設備狀態(tài)的油浸變壓器故障率模型。本文提出的模型中,比例故障率模型的基準故障率函數(shù)采用常用的溫升老化模型,連接函數(shù)中的協(xié)變量選擇了能夠全面客觀反映設備狀況的油中溶解氣體信息,然后推導了故障前時間的概率密度分布,并給出了使用極大似然估計擬合參數(shù)的方法。通過算例證明了提出模型的正確性。 支持向量機(Support Vector Machine, SVM)可用于變壓器故障診斷,針對現(xiàn)有SVM方法在樣本故障特征不明顯情況下有誤分類的情況,提出了一種基于支持向量機多分類概率輸出的變壓器故障診斷方法,此方法可以得到發(fā)生不同類型故障的可能性,即故障類別的概率,經(jīng)過進一步分析后給出診斷結論。算例表明本方法在繼承了SVM方法優(yōu)點的基礎上,提供了概率信息,對現(xiàn)有SVM方法誤診斷樣本也能給出可能存在的故障,彌補了現(xiàn)有SVM方法在變壓器故障特征不明顯條件下的不足。 在電網(wǎng)公司“調控一體化”的大背景下,進一步挖掘分析設備監(jiān)測信息,為給調控中心設定潛在事故預警預案、制定運行方式、合理安排檢修計劃、優(yōu)化調度策略提供基礎,實驗室項目組開發(fā)了“基于輸變電設備可載性分析的智能電網(wǎng)風險評估與決策系統(tǒng)”。本文介紹了此系統(tǒng)的功能,然后重點介紹和展示了筆者開發(fā)的輸變電設備健康評估功能中變壓器狀態(tài)評估和故障診斷模塊、基于SVG的信息展現(xiàn)模塊。
[Abstract]:Power transformer is the core part of the power network, and also an important asset of the power grid company. Its safe and stable operation is of great significance. On the basis of real-time monitoring of transformer status and operating conditions. The failure rate of transformer can be evaluated, and maintenance measures can be arranged reasonably to reduce the risk of equipment failure. In addition, fault diagnosis can be carried out for transformers that have been out of service or have high failure rate. Finding the fault cause quickly and taking the corresponding repair measures can effectively reduce the maintenance time and reduce the economic loss caused by the transformer outage. In this paper, the fault rate modeling method and fault diagnosis method of oil-immersed transformer are studied as follows. Proportional Hazard Model based on proportional failure rate model. PHM) and dissolved gas information in oil a failure rate model for oil-immersed transformers considering aging and equipment state is proposed. The benchmark failure rate function of the proportional failure rate model adopts the commonly used temperature rise aging model and the covariable in the connection function selects the dissolved gas information in the oil which can reflect the equipment condition comprehensively and objectively. Then, the probability density distribution of time before failure is deduced, and the method of using maximum likelihood estimation to estimate fitting parameters is given, and the correctness of the proposed model is proved by an example. Support Vector Machine support Vector Machine can be used in transformer fault diagnosis. In this paper, a transformer fault diagnosis method based on multi-classification probability output of support vector machine (SVM) is proposed to solve the problem that the existing SVM method has wrong classification under the condition that the sample fault feature is not obvious. The probability of different types of faults, that is, the probability of fault types, can be obtained by this method. After further analysis, a diagnosis conclusion is given. The example shows that this method inherits the advantages of SVM method. The probabilistic information is provided, and the possible faults can be given for the existing SVM method, which can make up for the deficiency of the existing SVM method under the condition that the fault characteristics of the transformer are not obvious. Under the background of "the integration of regulation and control" of the power grid company, the monitoring information of the equipment is further excavated and analyzed, the potential accident warning plan is set up for the control center, the operation mode is worked out, and the maintenance plan is arranged reasonably. The laboratory project team has developed a smart grid risk assessment and decision system based on load analysis of power transmission and transformation equipment. The functions of the system are introduced in this paper. Then the transformer condition assessment and fault diagnosis module and the information display module based on SVG are introduced and displayed in the health evaluation function of transmission and transformer equipment developed by the author.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM411

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本文編號：1407396