Robust frequency estimation in three‐phase power systems using correntropy‐based adaptive filter. Issue 8 (1st November 2015)

• Record Type: Journal Article
• Authors: Khalili, Azam; Rastegarnia, Amir; Sanei, Saeid
• Is Part Of: IET science, measurement & technology. Volume 9:Issue 8(2015)
• Page Start: 928
• Page End: 935
• Publication Date: 2015-11-01
• Subjects: frequency estimation -- power system parameter estimation -- adaptive filters -- impulse noise -- random noise -- maximum entropy methods -- gradient methods -- higher order statistics -- power supply quality -- harmonics suppression; robust adaptive algorithm -- robust frequency estimation -- three‐phase power systems -- random noise source -- Clarke's transformed three‐phase voltage -- augmented complex statistics -- model parameter estimation -- unbalanced system conditions -- linear predictive model -- maximum correntropy criterion -- gradient‐ascent optimisation -- computational complexity -- complex LMS algorithm -- higher‐order moments -- augmented CLMS algorithm -- voltage sags -- higher‐order harmonics -- impulsive noise -- correntropy‐based adaptive filter; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2015.0018
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : In this study, the authors propose a robust adaptive algorithm for frequency estimation in three‐phase power systems when the voltage readings are corrupted by random noise sources. The proposed algorithm employs the Clarke's transformed three‐phase voltage (a complex signal) and augmented complex statistics to deal with both of balanced and unbalanced system conditions. To derive the algorithm, a widely linear predictive model is assumed for the Clarke's transformed signal where the frequency of system is related 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 complex least‐mean‐squares (CLMS) algorithm, along with the robustness that is obtained by using higher‐order moments beyond just second‐order moments. They compare the performance of the proposed algorithm with a recently introduced augmented CLMS (ACLMS) algorithm in different 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 especially when the measured voltages are corrupted by impulsive noise.