【摘要】：電力變壓器是電力系統(tǒng)中重要的輸變電設(shè)備之一,在電力系統(tǒng)中處于樞紐地位,其運(yùn)行的安全可靠性直接關(guān)系到電力系統(tǒng)的安全。對變壓器進(jìn)行準(zhǔn)確的故障診斷,把握變壓器的狀態(tài),對于保證其安全穩(wěn)定運(yùn)行有著重要的意義。變壓器潛伏性故障發(fā)展時間較長,目前比較有效的檢測手段是油中溶解氣體分析技術(shù)(Dissolved Gas Analysis,DGA),可以發(fā)現(xiàn)變壓器是否發(fā)現(xiàn)故障,區(qū)分故障類別,并且對故障的嚴(yán)重程度進(jìn)行判斷。此外,對變壓器的潛伏性故障故障率做出準(zhǔn)確的估計,對于指導(dǎo)變壓器的運(yùn)維等生產(chǎn)活動也有著重要的意義�；谝陨媳尘�,本文主要圍繞變壓器故障診斷和潛伏性故障的故障率估計方法展開研究。本文首先分析變壓器故障類型以及變壓器故障時油中溶解氣體的特征,并對目前應(yīng)用于電力設(shè)備故障診斷的人工智能方法及其優(yōu)缺點進(jìn)行綜述;根據(jù)極限學(xué)習(xí)機(jī)方法的特點及優(yōu)勢,建立了基于極限學(xué)習(xí)機(jī)的故障分類模型,通過算例驗證該模型在故障診斷方面的有效性,為基于DGA的變壓器故障診斷提供一種新思路。然后,本文在熱動力學(xué)中焓的概念基礎(chǔ)上,通過對變壓器油分解化學(xué)反應(yīng)進(jìn)行分析,根據(jù)標(biāo)準(zhǔn)生成焓對不同故障氣體進(jìn)行加權(quán),引入能量加權(quán)DGA(Energy Weighted DGA,EWDGA)概念,分析其在變壓器故障嚴(yán)重程度判斷方面的應(yīng)用,并利用算例說明EWDGA在判別故障程度時比傳統(tǒng)的產(chǎn)氣率數(shù)據(jù)有著更客觀的判斷。最后,本文基于EWDGA和馬爾可夫過程,提出計及能量加權(quán)產(chǎn)氣率數(shù)據(jù)的變壓器潛伏性故障的故障率估計方法,并通過算例說明本文提出的方法與傳統(tǒng)方法相比,能夠更好的區(qū)分產(chǎn)氣率相同的狀態(tài)下,變壓器不同故障發(fā)展程度時的潛伏性故障的故障率。
[Abstract]:Power transformer is one of the important transmission and transformation equipment in power system. It is in the pivotal position in the power system. The safety and reliability of its operation is directly related to the security of power system. It is of great significance to make accurate fault diagnosis and grasp the state of transformer to ensure its safe and stable operation. The latent fault of transformer has a long development time. At present, the more effective detection method is dissolved gas analysis technology (Dissolved Gas Analysis,DGA) in oil. It can find out whether the transformer finds fault, distinguish the type of fault, and judge the severity of fault. In addition, it is of great significance to estimate the latent failure rate of transformers for guiding the operation and maintenance of transformers. Based on the above background, this paper focuses on transformer fault diagnosis and fault rate estimation of latent faults. This paper first analyzes the types of transformer faults and the characteristics of dissolved gases in oil when transformer faults occur, and summarizes the current artificial intelligence methods used in fault diagnosis of power equipment and their advantages and disadvantages. According to the characteristics and advantages of extreme learning machine, a fault classification model based on ultimate learning machine is established. The validity of the model in fault diagnosis is verified by an example, which provides a new way for transformer fault diagnosis based on DGA. Then, based on the concept of enthalpy in thermodynamics, by analyzing the decomposition chemical reaction of transformer oil, according to the standard enthalpy of formation, the concept of energy weighted DGA (Energy Weighted DGA,EWDGA is introduced. The application of EWDGA in judging the fault severity of transformer is analyzed, and an example is given to show that EWDGA has a more objective judgment than the traditional gas production rate data in judging the fault degree. Finally, based on EWDGA and Markov process, a fault rate estimation method for transformer latent faults considering energy-weighted gas production rate data is proposed, and an example is given to illustrate the comparison between the proposed method and the traditional method. It can better distinguish the failure rate of latent faults under the condition of the same gas production rate and different development degree of transformer faults.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TM407

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