Title: Orthogonal Matching Pursuit and K-SVD for Recovery of Sparse Power System Harmonics with the Cubature Kalman Filter
Abstract:With the knowledge of adverse effects of harmonics in power systems, parameters (amplitude, frequency, phase, and harmonic contents) estimation of a signal have attracted a rising interest. In recent ...With the knowledge of adverse effects of harmonics in power systems, parameters (amplitude, frequency, phase, and harmonic contents) estimation of a signal have attracted a rising interest. In recent years sparse representation of a signal has shown a growing interest over the existing techniques that follow Shannon/Nyquist sampling theorem, which requires large storage space, huge computational time, and moreover, the overall process is cost-effective. Keeping in mind the flaws of the conventional methods, this paper presents a novel sparse power system domain based cubature Kalman filter (SPSD-CKF) algorithm, which is the completely new framework that exploits the orthogonal matching pursuit (OMP), a sparse coding algorithm, and K-SVD, a learned dictionary, for sparse representation of a signal. The K-SVD contains the prototype signal-atoms where the signals are described by sparse linear combinations of these atoms, is flexible one and can work with any pursuit method. On the other hand, the OMP is easy to implement and takes less time. The CKF is utilized to estimate the amplitude, phase, frequency, and harmonics using fewer measurements in the sparse domain. To examine the efficacy of learning-based dictionary obtained using the K-SVD, some well-known static dictionaries such as Gabor dictionary (GD) and an overcomplete hybrid dictionary (OHD) have been adopted and their results are compared. Various simulation results suggest that the proposed algorithm provides an efficient mechanism to estimate the parameters and also robust against the noise.Read More
Publication Year: 2019
Publication Date: 2019-10-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 1
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