Adaptive master–slave unscented Kalman filter for grid voltage frequency estimation. Issue 4 (1st June 2018)

• Record Type: Journal Article
• Authors: Muñoz, Juan P.; Magaña, Mario E.; Cotilla‐Sanchez, Eduardo
• Is Part Of: IET signal processing. Volume 12:Issue 4(2018)
• Page Start: 496
• Page End: 505
• Publication Date: 2018-06-01
• Subjects: convergence -- measurement errors -- measurement uncertainty -- waveform analysis -- power system parameter estimation -- tracking filters -- power system stability -- power consumption -- frequency estimation -- nonlinear filters -- Kalman filters; adaptive master–slave unscented Kalman filter -- grid voltage frequency estimation -- time‐varying parameter -- power generation -- power consumption -- fundamental frequency -- under‐frequency relaying -- power system stabilisation -- adaptive algorithm -- master UKF -- tracking filter condition -- tracking accuracy -- convergence -- measurement noise covariance -- grid voltage waveform; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2016.0199
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : The frequency of the grid voltage is a time‐varying parameter caused by mismatches between power generation and power consumption. In fact, the fundamental frequency decreases when large loads are connected to the system or when a large generation source goes offline. The opposite holds true for an increase in the fundamental frequency, e.g. when generation exceeds consumption. Hence, in order to protect a power system against loss of synchronism, under‐frequency relaying, and power system stabilisation, accurate frequency estimation is necessary. This study proposes an adaptive algorithm based on a master–slave unscented Kalman filter (UKF) configuration to estimate both the voltage frequency and the measurement noise. Specifically, the master UKF uses the strong tracking filter condition to improve tracking accuracy, speed of convergence, and to desensitise the filter from initial conditions. The slave UKF uses the master UKF innovation to estimate the measurement noise covariance. The proposed approach addresses the tracking weaknesses of other frequency estimation algorithms when the frequency of the grid voltage waveform changes abruptly. Algorithm performance is measured through computer simulation.