Power System Traveling Wave Detection Analysis And Fault Location Based On Adaptive Filtering

With the rapid development of my country’s electric power industry,the scale of the power grid continues to expand,the topological structure of transmission lines has become more complex,the correlation coefficient of each power system has become higher and higher,and local transmission lines are prone to failure.If the fault cannot be quickly repaired,it will affect the normal operation of the power grid on a large scale.However,the occurrence of some short-circuit faults or lightning faults on transmission lines is inevitable,so it is necessary to quickly locate the fault point and shorten the line inspection time to reduce the loss caused by power outages.Nowadays,the technology for locating faults on transmission lines at home and abroad has been very mature,among which the traveling wave method is the most commonly used and high-precision fault locating technology.The key to traveling wave technology is the detection of traveling wave wave heads.This paper proposes a Kalman Filter Based on Maximum Likelihood(KF-ML)filtering algorithm based on the maximum likelihood for the detection of traveling wave signal wave heads.The main research results are as follows:Kalman filter algorithm(KF,Kalman Filter)has certain advantages in signal processing,and is widely used in power systems.It solves the problem of state estimation and parameter estimation under non-stationary conditions.But in the filtering process,the covariance matrix Q,R affects the accuracy of detection and the accuracy of estimation.Therefore,this paper adopts the online optimization of the noise covariance matrix Q,R and the initial state based on the maximum likelihood value.The estimated residual error obtained by KF-ML shows significant singularity and can be used to accurately determine the arrival time of the traveling wave head.The time recorded by the M and N detection points can be calculated using the double-ended traveling wave positioning method.The distance between the fault point and the detection point.(1)Through this method,the short-circuit fault traveling wave is located at different fault points,different resistances,different initial phases,and different noise interference conditions,and the short-circuit fault points are located on different initial phases with wavelet transform.Compared.The results show that,compared with KF,this method has stronger robustness and is insensitive to fault impedance and phase.(2)Lightning strikes are relative to short-circuit faults.Lightning strikes may cause line shorts or overvoltages to break down insulators due to flashover,and the lightning strike point and the fault point may not be at the same point.In this regard,this paper distinguishes the lightning strike point and the fault point based on the characteristics of the transient fault current,and uses KF-ML to detect the fault traveling wave to determine the lightning strike distance and the fault distance.The simulation results show that this method also has high accuracy and robustness in lightning fault location.The paper proposes an adaptive Kalman filter algorithm based on the maximum likelihood value,which overcomes the noise interference environment to a certain extent and improves the detection accuracy of traveling wave signals.In addition,by distinguishing the differences in current transient characteristics,faults can be dealt with quickly and effectively.Therefore,the method proposed in the thesis can provide certain theoretical guidance for the location of traveling wave faults in transmission lines.