基于改进K-means聚类的变压器异常状态识别模型

针对投运时间不长的变压器数据中有极大部分是正常数据的情况,为了有效利用变压器历史正常数据识别变压器是否异常,本文提出了基于改进K-means聚类的变压器异常状态识别模型。针对变压器绝大部分运行数据为正常数据、正常数据逐渐按一定的趋势变化以及异常状态数据变化急剧等特点,基于历史正常数据与K-means算法建立变压器异常状态识别模型；根据对正常数据聚类的结果确定用于识别新数据的各个阈值；通过计算新数据到各聚类中心的距离并与各阈值对比确认变压器是否异常。针对传统K-means算法的缺点,对K-means算法进行基于密度与距离选择K值与初始聚类中心的改进,使K-means算法有稳定的K值与聚类中心,聚类过程更加快速、稳定、有效,从而使识别模型计算得到的阈值更可靠。实例分析表明,模型能有效对变压器的异常状态进行快速、准确的识别,为变压器状态评估提供一种新思路。

Power Transformer Abnormal State Recognition Model Based on Improved K-means Clustering

For the situation that most of the data of transformers with low running time are normal data, in order to utilize the historical normal state data to identify efficiently whether the new data is abnormal, a power transformer abnormal state recognition model based on improved K-means clustering, is proposed in this paper. Now that most of the power transformer operation data are normal state data, and the normal state data gradually changes according to a certain trend while the abnormal state data changes rapidly, based on the historical normal data and K-means clustering, a recognition model for power transformer is established. According to the clustering results of normal data, thresholds can be acquired to identify new data .By calculating the distances from the new data to the cluster centers and comparing the distances with the thresholds to determine whether the transformer is abnormal. To solve the problem of traditional K-means algorithm, an improvement about choosing K value and initial cluster center based on data density and distance is proposed, which can give K-means clustering stable cluster centers and K value to make the procession of clustering quicker, more stable and efficient. The example analysis shows that the recognition model can effectively identify the abnormal state of the power transformer quickly and accurately and that the model provides a new idea for the state assessment of transformers.