Transformer winding deformation diagnosis method based on dynamic time warping and multilayer perceptron

To address the issues of strong subjectivity and difficulty in feature extraction that are inherent to traditional frequency response analysis methods used for diagnosing transformer winding deformation, an intelligent diagnostic method is proposed based on Dynamic Time Warping (DTW) and a Multilayer Perceptron (MLP). First, the frequency response curve is normalized and segmented into multiple frequency bands to extract physically meaningful features. Subsequently, the Dynamic Time Warping algorithm is employed to perform nonlinear curve alignment and difference quantification processes, thereby enhancing robustness against frequency-axis misalignment and measurement noise. Finally, the extracted features are fed into a Multilayer Perceptron (MLP) model, which utilizes multilayer nonlinear mappings to automatically identify the deformation levels of the windings. Validation based on field measurement data indicates that the proposed method achieves significant improvements in diagnostic accuracy, balance, and robustness when compared with traditional correlation coefficient methods and other machine learning models. This approach enables high-precision automated diagnosis of transformer winding deformation, offering a physically interpretable reference for condition monitoring as well as intelligent operation and maintenance of power equipment.