Detection of Transformer Winding Condition Based on Optimal K-Means Algorithm

To detect the mechanical status of transformer winding more accurately, we propose that the optimal K-Means clustering al-gorithm is adopted to analyze the unstable and time-varying vibration signals of transformer under sudden short-circuit. Firstly, the characteristic vectors of each vibration signal are abstracted. Then the data density is introduced to optimize the K-means algorithm, which can more optimize the selection of initial centroids. The centroids of the characteristic vectors un-der different conditions can be calculated based on the optimized K-Means algorithm. The experimental results of vibration signals for a 110 kV transformer under sudden short-circuit show that the optimized K-Means algorithm has the advantage of high accuracy. The variation of centroid positions of characteristic vectors is capable of clearly reflecting the variation de-gree of winding deformation, which can provide the reference for the proposal of condition maintenance strategy. © 2018, High Voltage Engineering Editorial Department of CEPRI. All right reserved.