本文選題：變壓器 + 漏磁場��； 參考：《華北電力大學》2017年碩士論文

【摘要】：變壓器作為一種傳輸和變換電能的重要設(shè)備,其繞組在運行過程中受到多次外部短路電動力的沖擊可能會發(fā)生不可恢復的形變,這種形變已成為引發(fā)變壓器繞組匝間短路故障的首要原因,因此,對變壓器繞組形變進行及時準確地在線監(jiān)測具有重大的現(xiàn)實意義。本文根據(jù)變壓器繞組發(fā)生形變后,將直接影響繞組附近的磁場分布,提出通過在線測量變壓器磁場的分布,間接實現(xiàn)對變壓器繞組形變的在線監(jiān)測。首先,對變壓器繞組形變與其磁場分布的對應關(guān)系進行研究。用ANSYS建立變壓器繞組正常及常見的形變模型,從定性和定量角度分析變壓器繞組形變與其磁場分布的對應關(guān)系。結(jié)果表明,變壓器磁場分布隨著繞組形變種類的不同而呈現(xiàn)不同的特征。其次,提出一種基于磁場分布特征的變壓器繞組形變分類法。根據(jù)繞組形變前后變壓器磁場的定量分析結(jié)果,定義五種用于判定變壓器繞組形變類型的系數(shù),以實現(xiàn)對變壓器繞組形變的分類。結(jié)果表明,該方法可以直觀、準確地對變壓器繞組的形變進行分類。然后,提出一種基于支持向量機的變壓器繞組形變分類法。用ANSYS仿真得到的變壓器繞組形變樣本對支持向量機進行訓練和測試,采用交叉驗證和網(wǎng)格搜索法對支持向量機的參數(shù)進行優(yōu)化。根據(jù)傳感器安裝方案對繞組形變分類正確率的影響,用改進粒子群算法對磁場傳感器安裝進行優(yōu)化。結(jié)果表明,該方法可以在安裝較少傳感器的情況下,實現(xiàn)對變壓器繞組形變的準確分類。最后,提出對變壓器繞組形變程度的判定方法。用歐式距離的大小衡量變壓器繞組形變的嚴重程度,根據(jù)粒子群算法得到的最優(yōu)傳感器安裝方案,通過分析變壓器繞組不同類型形變的嚴重程度與歐式距離的關(guān)系,提出對變壓器各種類型形變嚴重程度的判定方法,并給出變壓器繞組形變的判定閾值。
[Abstract]:As an important equipment for transmitting and converting electric energy, transformer windings may undergo irrecoverable deformation when they are impacted by external short-circuit electromotive force many times during operation. This kind of deformation has become the primary cause of transformer winding inter-turn short circuit fault. Therefore, it is of great practical significance to timely and accurately monitor transformer winding deformation on line. According to the fact that the magnetic field distribution near the transformer winding will be directly affected by the deformation of the transformer winding, this paper puts forward that the on-line monitoring of the transformer winding deformation can be indirectly realized by on-line measuring the distribution of the transformer magnetic field. Firstly, the relationship between transformer winding deformation and magnetic field distribution is studied. The normal and common deformation models of transformer windings are established by ANSYS. The relationship between transformer winding deformation and magnetic field distribution is analyzed qualitatively and quantitatively. The results show that the magnetic field distribution of transformer presents different characteristics with different types of winding deformation. Secondly, a method of transformer winding deformation classification based on magnetic field distribution is proposed. According to the quantitative analysis results of transformer magnetic field before and after winding deformation, five coefficients used to judge transformer winding deformation type are defined in order to realize the classification of transformer winding deformation. The results show that the method can classify transformer winding deformation intuitively and accurately. Then, a transformer winding deformation classification method based on support vector machine (SVM) is proposed. The support vector machine is trained and tested by the transformer winding deformation samples simulated by ANSYS. The parameters of the support vector machine are optimized by cross-validation and grid search. According to the effect of sensor installation scheme on the accuracy of winding deformation classification, an improved particle swarm optimization algorithm is used to optimize the magnetic field sensor installation. The results show that the method can accurately classify transformer winding deformation with fewer sensors installed. Finally, a method to judge the degree of transformer winding deformation is proposed. The magnitude of Euclidean distance is used to measure the severity of transformer winding deformation. According to the optimal sensor installation scheme obtained by particle swarm optimization, the relationship between the severity of different types of deformation of transformer windings and Euclidean distance is analyzed. A method for judging the severity of various types of transformer deformation is presented, and the threshold value of transformer winding deformation is given.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM41

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本文編號：2013852