【摘要】：電力變壓器是電力系統(tǒng)的重要組成成分，它的運行情況直接關(guān)系到電力系統(tǒng)總體的安全性和穩(wěn)定性。由于其內(nèi)部結(jié)構(gòu)的復(fù)雜性，運行環(huán)境的特殊性，在變壓器長期運行中，發(fā)生故障是不可避免的。隨著社會對供電質(zhì)量、可靠性、安全性要求的提高，研究開發(fā)電力變壓器的故障診斷技術(shù)對提高電力系統(tǒng)運行可靠性和科學(xué)管理水平是十分重要的。 在研究基于DGA(Dissolved Gas Analysis,油中溶解氣體分析)的變壓器問題診斷技術(shù)現(xiàn)狀的基礎(chǔ)上，傳統(tǒng)經(jīng)典的三比值法存在比值邊界模糊的缺點，因此智能故障診斷技術(shù)已成為研究趨勢。論文提出了將SA(Simulated Annealing,模擬退火算法)和SVM(SupportVector Machine,支持向量機)相結(jié)合，用模擬退火算法優(yōu)化支持向量機參數(shù)，，獲得模擬退火支持向量機模型(記作SA-SVM)的思想，以提高變壓器故障診斷準(zhǔn)確率。 論文首先對基于DGA變壓器故障診斷技術(shù)開展了探討，分析了以往各種比值法的優(yōu)勢和劣勢，以此為前提探討了人工智能變壓器故障診斷的必要性。為了全面地反映變壓器內(nèi)部故障與特征氣體之間的關(guān)系，提出采用5種特征氣體濃度比值共計15組數(shù)據(jù)作為特征預(yù)輸入量，并采用RFE(Recursive Feature Elimination,回歸特征消去)算法對15個特征量進行篩選，將篩選后特征量作為最終故障診斷模型的輸入。在支持向量機分類器模型的建立中，深入研究與之相關(guān)的支持向量機多分類方法、支持向量機核函數(shù)選擇以及支持向量機參數(shù)尋優(yōu)等問題。在對支持向量機分類器分類效果影響最大的參數(shù)尋優(yōu)問題上，引入模擬退火算法進行參數(shù)尋優(yōu)，獲得模擬退火支持向量機參數(shù)優(yōu)化流程。最后，以基因選擇算法篩選后的特征子集為輸入，變壓器故障診斷類型為輸出，獲得基于RFE-SA-SVM的變壓器故障診斷模型。為了避免在Matlab下編程函數(shù)句柄的抽象性，給出其故障診斷模型的GUI界面。通過該診斷模型與單一模型的對比驗證，顯示了所建立的RFE-SA-SVM模型的優(yōu)越性。使用該模型進行實例分析，驗證了該模型故障診斷方法的有效性，并具有一定的應(yīng)用價值。
[Abstract]:Power transformer is an important component of power system, its operation is directly related to the overall security and stability of power system. Due to the complexity of its internal structure and the particularity of the operating environment, it is inevitable that the transformer will fail in the long run. With the improvement of power supply quality, reliability and safety, it is very important to study and develop the fault diagnosis technology of power transformer to improve the reliability of power system operation and the level of scientific management. Based on the research of transformer diagnosis technology based on the analysis of dissolved gas in DGA (Dissolved Gas Analysis, oil, the traditional three-ratio method has the shortcoming of fuzzy ratio boundary, so intelligent fault diagnosis technology has become the research trend. In this paper, the idea of combining SA (Simulated Annealing, simulated annealing algorithm with SVM (SupportVector Machine, support vector machine to optimize support vector machine parameters and obtain simulated annealing support vector machine model (SA-SVM) is proposed. In order to improve the accuracy of transformer fault diagnosis. In this paper, the fault diagnosis technology of transformer based on DGA is discussed, and the advantages and disadvantages of each ratio method in the past are analyzed. The necessity of transformer fault diagnosis based on artificial intelligence is discussed. In order to fully reflect the relationship between the internal fault of transformer and the characteristic gas, a total of 15 groups of data of five kinds of characteristic gas concentration ratio are proposed as the characteristic pre-input quantity. The RFE (Recursive Feature Elimination, regression feature elimination algorithm is used to screen the 15 feature variables, and the filtered feature quantity is used as the input of the final fault diagnosis model. In the establishment of support vector machine classifier model, the related multi-classification methods of support vector machine, kernel function selection of support vector machine and parameter optimization of support vector machine are deeply studied. In the parameter optimization problem which has the greatest influence on the classification effect of support vector machine classifier, the simulated annealing algorithm is introduced to optimize the parameters, and the simulated annealing support vector machine parameter optimization flow is obtained. Finally, the transformer fault diagnosis model based on RFE-SA-SVM is obtained by taking the feature subset selected by gene selection algorithm as input and transformer fault diagnosis type as output. In order to avoid the abstraction of programming function handle in Matlab, the GUI interface of its fault diagnosis model is given. The superiority of the established RFE-SA-SVM model is demonstrated by comparing the diagnostic model with the single model. The effectiveness of the fault diagnosis method of the model is verified by using the model for example analysis, and it has certain application value.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM41

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