本論文提出應用頻率響應分析與漸進式支撐向量機於變壓器繞組匝間短路故障定位系統設計，並分別針對正常變壓器與不同故障位置之變壓器，蒐集頻率響應特性及輸入至漸進式支撐向量機進行訓練，以尋找短路故障發生位置。另外本文於變壓器頻率響應特性分析中，係利用複導體輸電線理論進行變壓器模型之建立，依此模擬取得運轉資料，進而用以漸進式支撐向量機之訓練，最後以交叉驗證方式評估本文所提系統之鑑別準確率。經測試結果顯示，本文所提方法應有助於維修人員偵測及掌握繞組匝間短路故障位置，應有助於減少事故發生機率，提升供電可靠度。

In this thesis, a frequency response analysis method embedded with a transductive support vector machines (SVM) is applied to detect and localize the short turn of transformer windings. In the method, a multiconductor transmission line method is applied to achieve the modeling of transformers, by which the frequency response data is extensively collected from different fault locations and applied as the training basis for transductive support vector machines. Meanwhile, a cross-validation method is used in the test process to increase the detection accuracy. This integrated approach has been applied to examine various scenarios with a satisfactory success. The outcome gained from this thesis is anticipated to be useful as a forewarning mechanism, thus helping decrease the event occurrence and increase the reliability of supplying power.

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