PROJECT TITLE :

Robust frequency estimation in three-phase power systems using correntropy-based adaptive filter

ABSTRACT:

In this study, the authors propose a strong adaptive algorithm for frequency estimation in three-section power systems when the voltage readings are corrupted by random noise sources. The proposed algorithm employs the Clarke's reworked 3-section voltage (a advanced signal) and augmented complicated statistics to accommodate each of balanced and unbalanced system conditions. To derive the algorithm, a widely linear predictive model is assumed for the Clarke's remodeled signal where the frequency of system is connected to the parameters of this model. To estimate the model parameters with noisy voltage reading, they utilise the notions of maximum correntropy criterion and gradient-ascent optimisation. The proposed algorithm has the computational complexity of the popular complicated least-mean-squares (CLMS) algorithm, along with the robustness that is obtained by using higher-order moments beyond simply second-order moments. They compare the performance of the proposed algorithm with a recently introduced augmented CLMS (ACLMS) algorithm in numerous conditions, including the voltage sags and presence of impulsive noises and and higher-order harmonics. Their simulation results demonstrate that the proposed algorithm provides improved frequency estimation performance compared with ACLMS particularly when the measured voltages are corrupted by impulsive noise.