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Detection of Grid Voltage Fundamental and Harmonic Components Using Kalman Filter Based on Dynamic Tracking Model

The Kalman filter (KF) algorithms based on traditional models, which, applied in real-time detection of grid voltage, have the margin to improve tracking accuracy. Their tracking models do not specify the covariance matrix of state noise in theoretical derivation. They can only be taken as a unit matrix. In this paper, a dynamic tracking model (DTM) is proposed. Further, a linear KF algorithm based on DTM model (DTM-KF) is presented. The proposed DTM-KF algorithm gives the covariance matrix of state noise and overcomes the defects of the traditional models based KF algorithms. It is compared with two traditional models–based KF algorithms by simulation and experimentation. The tracking accuracy of the fundamental component and the estimation accuracy of the harmonic components are analyzed and compared. The results show that the proposed DTM-KF algorithm has high tracking accuracy.