Tracking of power quality disturbances using sparse model–based extended Kalman filters

Abstract

Accurate estimation and tracking of power quality (PQ) disturbances requires efficient adaptive model-based techniques, which should have elegant structures to be applicable in practical systems. Though extended Kalman filter (EKF) has been used as a popular estimator to track the time-varying PQ events, the performance is limited due to higher-order nonlinearity exists in system dynamics.Moreover, the computational complexity and sensitivity to the measurement noise affect the estimation accuracy and error convergence properties. If the underlying sparse behavior of adaptive filters can be exploited through norm penalty, then significant improvement can be achieved in terms of convergence speed, stability, and steady state excess mean square error performance. In this paper, a modification to EKF is proposed by using sparse model to estimate PQ disturbances with better estimation accuracy and faster convergence speed.A thorough comparison is presented in terms of simulation results by using a family of existing algorithms like least mean square, recursive least square, and EKF. Adaptive estimation model incorporates real and complex state space representations to represent discrete time PQ disturbances. The convergence of the proposed sparse model-based filter is also mathematically proved considering the mean square error performance.

A Novel Extremum Seeking Hyperparameter Design for Joint State and Parameter Estimation of Nonlinear Systems

“…With proper design of the parameters, EKF is a fast and accurate method so that preferable for real-time applications. Recently, model-based EKF has been designed for power systems to track power quality [9], and applied for minimization of covariance matrices for efficient estimation of bilinear systems [10].…”

Online tracking of voltage flicker for inverter‐based distributed generation using Teager energy operator

“…To eliminate the problems of the FFT-based methods, various algorithms have been utilized to detect and evaluate PQ disturbances. These algorithms include least absolute value, 9,10 the extended Kalman filter, [11][12][13][14][15] the continuous wavelet transform, [16][17][18] the Hilbert transform, 19,20 the S transform, 21,22 the particle swarm optimization, 23 the artificial neural network, 24 the extreme learning machine 25,26 as well as a combination of several methods as a hybrid method. [27][28][29] However, the used methods present the desired ability to detect PQ disturbances with appropriate accuracy, but they have high complexity and computational burden as well as the requirement to adjust accurate method parameters.…”