An anomaly detection method for electric power information system based on improved k-means
Lin Huang, Jian Chang, Fan Yang, Yi Li, Xin-Zheng Niu · JOURNAL OF SHENZHEN UNIVERSITY SCIENCE AND ENGINEERING · 2020
The electric power information system is usually used to control the power equipment. The anomaly detection of electric power information system is very important for maintaining the stable operation of power equipment. However, the traditional anomaly detection method is difficult to detect the comprehensive anomalies of multi-indicator in the electric power information system. In order to solve this problem, an anomaly detection method based on improved k-means algorithm is presented in this paper. To reduce the amount of calculation, the data space is compressed by dividing the data space into multiple grids and all the sample points in same grid are mapped by the mean point of grid. In order to accurately identify the normal mode, the accuracy of k-means algorithm is improved by the mechanism of moving cluster boundaries based on the cluster boundary density and cluster density. Then the anomalies are detected by calculating the deviation degree between the data and normal mode. The experimental results show that the proposed method can accurately mine the comprehensive anomalies on multi-indicator. Compared with other anomaly detection methods, the running time of our method is reduced from 16.44 seconds to 0.55 seconds and the accuracy of anomaly detection is improved by 5.2%. Our method has good application prospects in the field of power operation and maintenance anomaly detection.