Research on Identification Method of Transformer Windings' Loose Vibration Spectrum Considering a Multi-Load Current Condition  
[PERSON]  
1 State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China; [EMAIL_ADDRESS] (J.F. ); [EMAIL_ADDRESS] (F.W)  
Xudong Deng  
2 Ultra-High Voltage Branch of State Grid Chongqing Electric Power Company, Chongqing 400050, China; [EMAIL_ADDRESS] (X.D. ); xjuco@18 nina.com (Y.X. ); [EMAIL_ADDRESS] (C.W. ); [EMAIL_ADDRESS] (Y.L. ); leeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee  
than 40% of transformer accidents are caused by winding faults [6], and the annual failure rate of transformers is between 0.49% and 9% [7]. Therefore, it is of great significance to detect and eliminate the early loosening faults of power transformer windings to maintain the normal operation of transformers. In recent years, the employment of the vibration method for the detection of the mechanical status of power transformers has drawn the focus of scholars across the globe and in domestic research circles. Unlike the traditional fault detection method, the vibration signal can realize electrical isolation and real-time online monitoring, which can maintain the secure and dependable operation of the power grid [8; 9]. At the moment, the classification of the mechanical fault state of power transformer windings based on vibration signals is mainly achieved by extracting one-dimensional vibration signal eigenvalues or two-dimensional feature map information, and by using machine learning or deep learning algorithms for classification and recognition. Ref. [10] collected the winding vibration signals under different operating conditions of transformers, extracted the signal spectrum entropy value by wavelet transform as the input feature vector, and trained and tested the feature quantity using support vector machine. The identification and diagnosis of transformer windings under different operating conditions were achieved. Ref. [11] collected the short-circuit fault vibration signals of transformer windings in different degrees under transient and steady-state operating conditions. The Short-Time Fourier Transform (STFT) [12] was employed to analyze the transient phase signal, and the Fourier transform was utilized for the analysis of the steady-state phase. The energy index, along with the total harmonic distortion index, was put forward for the training of the neural network, and then the accurate identification of different degrees of transformer winding faults was achieved. Ref. [13] proposes a diagnostic approach for transformer winding faults, relying on statistical time features (STFs) and support vector machine (SVM) [14]. Several indices in the vibration signal of the transformer were calculated as statistical time features. Fisher score analysis was used to analyze the most discriminative features, and linear discriminant analysis was applied to reduce the dimensions of features. In the end, the SVM was employed to accomplish automatic diagnosis. Ref. [15] proposed a residual attention diagnosis model for power transformer winding fault fusion based on vibration signals and designed a Gramian guided filtering module to generate and fuse two-dimensional images at different positions from the original vibration signal. A high-dimensional convolutional attention mechanism module for an improved deep residual network was proposed to conduct the diagnosis of transformer winding faults. Ref. [16] proposes a fault diagnosis method for the mechanical structure of power transformer windings based on comprehensive feature extraction and the Subtraction-Average-Based Optimizer (SABO) algorithm. Initially, the original vibration signal is extracted through a dual-feature approach using wavelet transform, and the optimized variational mode decomposition is implemented, which is based on the mean reduction optimizer algorithm (SABO).