| In recent years,with the collection and transmission of information has realized intelligent in substations,the traditional remote terminal microprocessor mainly uses single chip microcomputer,which has the characteristics of single function and weak processing performance,cannot meet the requirements of high-speed,real-time and long-distance communication.In order to solve the above problems,this thesis designed a remote terminal device,which is based on STM32F207ZET6,can collect 24 voltage signals and 24 current signals.At the same time,the device adopts multi-channel analog switch technology and multiple communication upload technology,ensure the rapidity and real-time of signal acquisition and upload.The specific research on data acquisition and processing are as follows:In the aspect of remote terminal signal acquisition,this thesis used the AC sampling technology which is based on Fourier algorithm.And calculated the effective value of voltage and current,active power,reactive power and other power parameters in the substation site.Then used the adaptive filtering method to reduce noise.Through the simulation verification of Wiener filtering and adaptive filtering,it is proved that LMS based adaptive filtering is better than Wiener filtering and has the characteristics of fast tracking of changing signals,and when the statistical characteristics of autocorrelation sequences are unknown,its convergence accuracy and tracking speed are relatively good.This thesis studied the characteristics of anomaly detection data set,used the sum of squares of errors to determine the optimal number of clusters K.And used setting threshold discrimination,data horizontal comparison and improved K-MEANS algorithm to realize the identification of abnormal data.After analyzd the shortcomings of the traditional K-MEANS clustering algorithm in anomaly detection,through simulation of these two clustering algorithms,it is proved that the improved K-MEANS clustering algorithm used in this thesis is superior to the traditional K-MEANS clustering algorithm in terms of detection rate and false alarm rate,and is suitable for the inspection of power quality anomaly data. |
