Grid synchronization is a critical concern for proper control of the energy transferred between the Distributed Power Generation System (DPGS) and the utility grid. Nonlinear estimation techniques are proposed to track the voltage magnitude, phase angle, and frequency of the utility grid. Instead of directly analyzing in abc coordinate frame, the symmetrical component is employed to separate the positive, negative, and zero sequences in the transformed αβ stationary coordinate frame. By using αβ stationary coordinate frame, the number of system state variables is reduced to five. The results show that our proposed nonlinear estimation technique is efficient in smart power system synchronization. The MATLAB simulation studies have been conducted to compare the performance of Extended Kalman Filter (EKF), Particle Filter (PF), and Unscented Kalman Filter (UKF). Computer simulations have shown that the efficacy of our proposed nonlinear estimation methods including the Extended Kalman Filter, Particle Filter, and Unscented Kalman Filter. It also shows that the Unscented Kalman Filter, and the Particle Filters are better estimators, because voltage synchronization problem is nonlinear system, and linearization process which the Extended Kalman Filter is based on is not very accurate. The number of particles in Particle Filter can be increased to improve the accuracy, but there exists a trade off between computational effort and estimation accuracy. In our research, with consideration the same computational complexity, we calculate the Mean Square Error (MSE) to examine the performances of different nonlinear estimation approaches. By comparing the MSE of different estimators, we prove that the Unscented Kalman Filter shows the most accurate performance in voltage synchronization for three phase unbalanced voltage. Our results have shown the potential applications in the future smart power grid synchronization.
A New Linear Regression Kalman Filter with Symmetric Samples
