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A new state-space for unbalanced three-phase systems: Application to fundamental frequency tracking with Kalman filtering

In nowadays electrical grids with distributed renewable energy generation, in microgrids and smart grids, the estimation of the fundamental frequency in a fast and precise way is essential. This paper introduces a new state-space model to be used with an Extended Kalman Filter (EKF) for estimating the frequency of power system signals in real-time. The proposed model takes into account all the characteristics of a general three-phase power system and mainly the unbalance. Therefore, the symmetrical components of the power system, i.e., their amplitude and phase angle values, can also be deduced at each iteration from the proposed state-space model. The effectiveness of the method has been evaluated. Results and comparisons of online frequency estimation and symmetrical components identification show the efficiency of the proposed method. The frequency is precisely estimated from time-varying signals disturbed by higher-order harmonics.