The frequency of a three-phase power system can be estimated by identifying the parameter
of a second-order autoregressive (AR2) linear predictive model for the complex-valued αβ
signal of the system. Since, in practice, both input and output of the AR2 model are observed
with noise, the recursive least-squares (RLS) estimate of the system frequency using this
model is biased. We show that the estimation bias can be evaluated and subtracted from the
RLS estimate to yield a bias-compensated RLS (BCRLS) estimate if the variance of the …

We examine the problem of estimating the frequency of a three-phase power system in an
adaptive and low-cost manner when the voltage readings are contaminated with
observational error and noise. We assume a widely-linear predictive model for the αβ
complex signal of the system that is given by Clarke's transform. The system frequency is
estimated using the parameters of this model. In order to estimate the model parameters
while compensating for noise in both input and output of the model, we utilize the notions of …