Abstract
This paper presents a new method for estimating the rate of change of frequency (RoCoF) of voltage or current signals measured using instrument transformers. The method is demonstrably superior to currently available methods in the literature, in terms of estimation latency and estimation error. The estimation is performed in two steps. In the first step, the analog voltage or current signal obtained from an instrument transformer is statistically processed using interpolated discrete Fourier transform (IDFT) in order to obtain the means and variances of the signal parameters. These means and variances are then given as inputs to the second step, in which Kalman filtering (KF) is used to find the final RoCoF estimate. Accurate mathematical expressions for the means and variances of signal parameters have been derived and used in the second step, which is the main reason behind the superior performance of the method. The applicability of the method has been demonstrated on a benchmark power system model.
Highlights
R ATE of change of frequency (RoCoF) is an important indicator for assessing the stability of energy networks and is used as an input for protection and control devices used in power systems
The current limiters of automatic voltage regulators (AVRs) can get triggered due to overcurrent or undercurrent observed during a high rate of change of frequency (RoCoF) period
The significance of fast and accurate estimation of RoCoF can be inferred from the fact that tripping of a RoCoF-relay played a key role in the UK power blackout of 2008 [1]
Summary
R ATE of change of frequency (RoCoF) is an important indicator for assessing the stability of energy networks and is used as an input for protection and control devices used in power systems. As noise and errors are invariably present in any measurement, an alternative solution for fast and accurate estimation of RoCoF is to use methods based on statistical signal processing, which work on the idea of filtering out noise and errors using their statistical properties. Several methods of RoCoF estimation have been proposed in literature utilizing various techniques of statistical signal processing [7]–[19] These techniques involve phase locked loop (PLL) ([7], [14]), Taylor-Fourier transform (TFT) and Taylor weighted least squares (TWLS) ([8], [13], [14], [16]–[19]), convolution and interpolation ([9]–[11], [15]) and Kalman filtering [12].
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