A method of voltage sag detection based on strong tracking SVD-UKF

Abstract

Abstract Addressing issues of the traditional strong tracking unscented Kalman filtering (STUKF) algorithm in the process of voltage sag detection, such as inadequate precision, large computational complexity, and unstable iterative procedures, a method of voltage sag detection based on strong tracking SVD-UKF is proposed. Only one unscented transformation (UT) is used per iteration by establishing the linear state equation based on the voltage signal model, which simplifies the calculation procedure. Substituting singular value decomposition (SVD) for Cholesky decomposition averts the challenge of non-positive-definite error covariance matrix, ensuring stable execution of the algorithm. The action position of the fading factor is improved to further enhance detection precision. Through simulation experiments, we validate that the proposed method could effectively detect voltage sag characteristics, including amplitude, phase, and duration time.