Harmonic Detection And Analysis Of Power System Based On Adaptive Filtering

In recent years,with the large-scale development and utilization of renewable energy and the wide application of various power electronic equipment,the harmonic problem of power grid is becoming increasingly prominent,fast and accurate harmonic state detection is very important.Kalman filter has unique advantages in dealing with linear,Gaussian system state filtering and parameter estimation,but its development and application have been plagued by noise statistical characteristics,initial state parameter selection and other issues.In this paper,an accurate harmonic detection method based on adaptive filter algorithm is proposed.The main research results of this paper are as follows:Firstly,the Kalman filter(KF)algorithm is studied systematically in the framework of Bayesian theory.Aiming at the problem of low detection accuracy under noise interference,a harmonic detection method based on adaptive Kalman filter residual analysis is proposed,the state space model of voltage signal is established first,the noise covariance matrix R,Q of system model and the initial state of system are adaptively optimized by maximum likelihood method,and the prediction and repair of harmonic signal are realized by KF,then get the best estimation of harmonic amplitude,using the singularity of filter residual can accurately detect the start and end time of harmonic interference.At the same time,in view of the limitation of Kalman filter algorithm in nonlinear system,the paper also proposes to use extended Kalman filter to track the dynamic change of the fundamental frequency,so as to realize the accurate detection of the fundamental frequency.Then,in view of the problem that the initial value of the parameters to be optimized in the adaptive Kalman filter algorithm based on the maximum likelihood is easy to fall into the local optimum,the paper further proposes a Kalman filter algorithm based on the adaptive particle swarm optimization genetic algorithm(PSO-GA-KF),and uses it for harmonic detection.By combining the Kalman filter algorithm based on maximum likelihood with PSO and GA to adaptively optimize the noise covariance matrix R,Q and other initial state parameters,the global optimal solution is obtained in a short search time,and the detection accuracy is improved.Through MATLAB software simulation,the paper compares the harmonic detection results of various algorithms under the condition of different noise intensity and different voltage amplitude parameters,and verifies the optimal performance of the algorithm.The two adaptive Kalman filtering algorithms proposed in this paper overcome the problem of low filtering accuracy caused by the uncertainty of noise statistical characteristics in the noise interference environment,and effectively improve the detection accuracy of power grid harmonics.In addition,the extended Kalman can accurately detect the fundamental frequency.Therefore,the research method of this paper can provide method guidance for harmonic detection and control of power system.