Estimating frequency of three-phase power systems via widely-linear modeling and total least-squares

The frequency of a three-phase power system can be estimated from the parameters of a widely-linear predictive model for the complex-valued αβ signal of the system. Using the total least-squares (TLS) method, it is possible to estimate the model parameters while compensating for error in both input and output of the model when the voltage readings of the three phases are contaminated with noise. In this paper, we utilize the inverse power method to find a TLS estimate of the parameters of the assumed widely-linear predictive model in an adaptive fashion. Simulation results show that the resultant algorithm, called augmented inverse power iterations (AIPI), outperforms the recently proposed augmented complex Kalman filter (ACKF) and augmented complex extended Kalman filter (ACEKF) algorithms in estimating the frequency of the three-phase power systems. Unlike ACKF and ACEKF, AIPI requires no parameter tuning or prior knowledge of the noise variances. Computational complexity of AIPI is also similar to those of ACKF and ACEKF.