Power quality disturbance signals must be continuously monitored, stored, and transmitted for effective analysis, protection, and system planning in modern power systems. The large volume of data generated during power quality monitoring necessitates efficient storage techniques. The sparse representation of power quality signals can significantly reduce memory requirements while preserving important signal characteristics. Since several techniques exist for obtaining sparse representations, it is important to identify the most suitable Sparse Signal Decomposition (SSD) technique for different power quality disturbances. This paper presents a comparative study of various SSD techniques, including Orthogonal Matching Pursuit (OMP), Matching Pursuit (MP), Least Squares–Orthogonal Matching Pursuit (LS-OMP), and Thresholding and Basis Pursuit (BP), along with diverse dictionaries for the representation of power quality disturbances such as sag, swell, transients, and harmonics. Mean Square Error (MSE) and the ratio between the actual signals and reconstructed signals (A/R ratio) are used to evaluate the accuracy, while computation time is considered to compare the computational speed of different techniques. Simulation studies are carried out in MATLAB to evaluate the effectiveness of the SSD techniques. From the simulation results, it is observed that OMP and LS-OMP provide accurate representations of power quality disturbance signals. For sag, swell, and transients, the impulse dictionary performs best with OMP, offering faster computation. However, for harmonics, OMP with DCT dictionary is found to be more effective.
Anjali et al. (Sat,) studied this question.