Abstract:

In this paper,by analyzing 60000high-frequency monitoring data of 10equipments in a thermal power plants,we find that abnormal data has two characteristics:outlier or abnormal segment.A denoising method based on the first-order forward difference and the frequency distribution to detect the outlier and abnormal data in the high-frequency is proposed.In this method the threshold of abnormal data is determined by the risk probability and the frequency distribution of absolute value of the first-order forward difference, and the maximum number of the outliers included in the abnormal segment is obtained according to the performance of the device and the sampling frequency. The judgment rule for detecting abnormal data is then given with the threshold and the maximum number of the outliers.The method is applied to 6000data of a thermal power plant pre-pump motor for showing its effectiveness.

Key words: Thermal power plant, Abnormal data, Denoising method, Frequency distribution, Forward difference