【摘要】：隨著電網(wǎng)規(guī)模的不斷擴大,電氣設(shè)備數(shù)量不斷增加,傳統(tǒng)的以定期檢修為主的檢修模式已經(jīng)無法滿足電網(wǎng)發(fā)展的要求。近些年來狀態(tài)檢修得到了越來越多的關(guān)注,這種方法通過在線監(jiān)測數(shù)據(jù)及時發(fā)現(xiàn)設(shè)備異常,并對故障部位進行有針對性的維修。但變壓器結(jié)構(gòu)復(fù)雜,現(xiàn)場數(shù)據(jù)繁多,在分析數(shù)據(jù)進行變壓器狀態(tài)評估與故障診斷的理論研究方面仍存在很多不足。 本文以信息熵理論為基礎(chǔ),結(jié)合成熟的故障樹模型以及油色譜診斷技術(shù),對變壓器檢修方案優(yōu)化進行研究。通過計算故障樹信息熵值,得出國網(wǎng)檢修導(dǎo)則中實驗項目的信息熵值；根據(jù)每種檢測方法的信息熵值,對變壓器檢修方案進行優(yōu)化,建立基于信息熵理論的變壓器狀態(tài)檢修策略優(yōu)化模型。在綜合考慮測試結(jié)果、檢測方法難易程度和故障概率等因素的情況下,對變壓器檢測項目進行排序。提高檢修效率的同時,最大限度的減少檢修次數(shù)降低檢修成本。 根據(jù)變壓器故障歷史統(tǒng)計數(shù)據(jù),以變壓器油中溶解氣體參數(shù)為特征向量,在傳統(tǒng)的灰色關(guān)聯(lián)度理論的基礎(chǔ)上結(jié)合信息熵得到一種新的變壓器故障診斷方法。通過對每種特征氣體信息熵值的計算,對傳統(tǒng)的特征氣體參考序列因子權(quán)重值進行修訂。這種新的基于事故概率的參考因子,避免的以往灰色理論診斷中的人為因素影響,有效提高了故障診斷的客觀性和準(zhǔn)確性。
[Abstract]:With the continuous expansion of the scale of power grid and the increasing number of electrical equipment, the traditional maintenance mode based on periodic maintenance has been unable to meet the requirements of the development of power grid. In recent years, more and more attention has been paid to condition-based maintenance (CBM). This method detects equipment anomalies in time through on-line monitoring data, and makes targeted maintenance of fault sites. However, the structure of transformer is complex and the field data is various, so there are still many deficiencies in the theoretical research of transformer condition evaluation and fault diagnosis by analyzing the data. Based on the information entropy theory and the mature fault tree model and oil chromatographic diagnosis technology, this paper studies the optimization of transformer overhaul scheme. By calculating the information entropy value of the fault tree, the information entropy value of the experimental items in the national network maintenance guidelines is obtained, and the transformer maintenance scheme is optimized according to the information entropy value of each detection method. The optimal model of transformer condition maintenance strategy based on information entropy theory is established. In the case of considering the test results, the degree of difficulty and the probability of failure, the items of transformer detection are sorted. At the same time, improve the efficiency of maintenance, minimize the number of maintenance and reduce the cost of maintenance. Based on the historical statistical data of transformer faults and taking dissolved gas parameters in transformer oil as eigenvector, a new method of transformer fault diagnosis is obtained based on the traditional grey correlation degree theory and information entropy. By calculating the entropy value of each characteristic gas information, the weight of the traditional characteristic gas reference series factor is revised. This new reference factor based on accident probability avoids the influence of human factors in previous grey theory diagnosis and effectively improves the objectivity and accuracy of fault diagnosis.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM407

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本文編號：2252223