Adaptive master–slave unscented Kalman filter for grid voltage frequency estimation

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, underfrequency relaying, and power system stabilization, accurate frequency estimation is necessary. This paper proposes an adaptive algorithm based on a master-slave unscented Kalman filter (MS-UKF) configuration to estimate both the voltage frequency and the measurement noise. Specifically, the master UKF uses the strong tracking filter (STF) condition to improve tracking accuracy, speed of convergence, and to desensitize the filter from initial conditions. The slave UKF uses the master UKF innovation to estimate the measurement noise covariance. Our 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.

Combination of IMM Algorithm and ASTRWCKF for Maneuvering Target Tracking

“…It improves the stability of the algorithm by introducing time-varying factors into the residual sequence to achieve the corresponding functions. The formula is as follows [36][37] where b is the decay factor, which usually can be taken in the range of 95 . 0 7 .…”

An Instantaneous Frequency Identification Algorithm for Time-Varying Frequency Signals

“…According to Fig.1, the phase difference between two adjacent zero points is 180 • . When 7and (8) are used to describe the nonlinear time-varying frequency, according to (9) and (10), within the time intervals t 01 -t 04 , we established (21) and (22), as shown at the bottom of the next page.…”

“…In recent years, time-varying frequency identification has attracted the attention of researchers, and some kinds of frequency-modulated signals probably existed in the power system have been discussed in [16]- [21]. Reference [16] proposed a complex-valued least squares framework to improve the accuracy of the smart discrete Fourier transform, which has a certain degree of adaptability to time-varying frequency.…”

“…However, for the time-varying frequency signal, it only considered the frequency modulated by a linear function. An adaptive algorithm based on UFK was presented to estimate the linear and sinusoidal frequency in [21]. References [16]- [21] did not consider the cases in which the frequency is modulated by higher-order time polynomials.…”

“…An adaptive algorithm based on UFK was presented to estimate the linear and sinusoidal frequency in [21]. References [16]- [21] did not consider the cases in which the frequency is modulated by higher-order time polynomials. For the proposed algorithms in [16], [18], [19], the specific form of the time-varying frequency signals whose frequencies are modulated by different functions and the definition of IF were not considered.…”