Anomalous State Detection of Power Transformer Based on K-Means Clustering Algorithm

As an important hub equipment of power system, the safe and stable operation of transformer is the top priority to ensure the continuous supply of high-quality electric energy and the normal operation of social life. The state estimation of the transformer is the key to the operation state maintenance method. The existing transformer state estimation methods mainly use gas content and other data, but can not use the massive transformer electrical quantity monitoring data accumulated in the monitoring system. Therefore, a k-means clustering method for transformer state anomaly detection based on voltage, current and power data of transformer is proposed. Firstly, based on the monitoring data of transformer with normal maintenance history, a state detection model based on K-means clustering is constructed. Then, according to the clustering results of historical normal data, the appropriate threshold is selected, and the distance between the new data and each cluster center is analyzed to judge the operation status of the transformer. Finally, the correctness of the model is verified by an example. The results show that the proposed method can make full use of the electrical data of the transformer and realize the real-time detection of the transformer state, which is convenient for engineering application.