Estimating the frequency in power system based on state space recursive least squares

A new technique for estimating the frequency in power system is proposed in this paper. The standard recursive least squares (RLS) has the fast rate of convergence and is not sensitive to variations in the eigenvalue spread of the correlation matrix of the input vector, however, the tracking performance of RLS is limited in nonstationary condition. State space recursive least squares (SSRLS) technique allows the designers to choose an appropriate model to describe the information of system, so it can track the time-varying system. Considering the unbalance faults in power system, the complex voltage vector model formed by alphabeta - transformation is used as the frequency estimation model. On the other hand, the exponent-smoothing technique can reduce the errors caused by noise and oscillatory, so it is natural to use the exponent-smoothing technique to adjust the phase estimated by SSRLS. The results show that the proposed method based on SSRLS and exponent-smoothing technique gives the accurate frequency estimation even under the low signal-to-noise and the nonstationary condition.