本論文運用機器學習辨識變壓器異常電流類型。當變壓器空載投入系統或外部故障清除後電壓恢復時，因為殘餘磁通導致鐵心的飽和且可能造成一暫態電磁湧入電流，但跟變壓器發生外部故障時的故障電流不同，所以如何辨識異常電流類型，並避免保護電驛誤動作是相當重要的。湧入電流及故障電流擁有不同的特徵，將這些特徵當作類神經網路的輸入資料，類神經網路處理單元的轉移函數採用正切對數雙彎曲函數，並以多層前授型網路為架構，以辨識變壓器異常電流類型。
本論文利用Matlab Simulink來模擬變壓器湧入電流和變壓器發生外部故障的狀況，將模擬後的電流透過離散小波轉換擷取訊號中重要的資訊，再透過計算其統計特徵值當作類神經網路的輸入資料，透過類神經網路的學習與回想，最後可辨識變壓器的異常電流類型。本文所提出的方法確實可行，且辨識結果有不錯的準確度。

This thesis using machine learning to classify the abnormal current types of transformer. When the transformer connect to the power system or when the external faults is cleared, the residual magnetic flux cause the transformer core to saturate and may cause a transient electromagnetic inrush current. The inrush current is different from the external faults current, so how to classify the abnormal current types, and prevent the protection relay to malfunctioning is important. Inrush current and external faults current have different characteristics, and these characteristics are regarded as input data of neural network. The transfer function of neural network processing unit adopts tangent hyperbolic function, and is structured by the multi-layer feedforward network. In order to classify the abnormal current types of the transformer.
This thesis using Matlab Simulink to simulate the inrush current and the external faults of the transformer. The simulated currents using discrete wavelet transform to capture the important data of the signals, and then the statistical features are calculated as the input data of neural network. After the neural network learning and recalling. Finally it can classify the abnormal current types of the transformer. The method proposed in this thesis is feasible, and the classification results have good accuracy.

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