Power quality disturbances reconstruction method based on ATSAMP

Abstract

As the number of power quality monitoring devices connected to the power grid increases, the volume of collected power quality data has grown substantially. This rapid growth poses significant challenges for data transmission and storage within the grid. The sparse adaptive matching pursuit (SAMP) algorithm, a compressed sensing (CS) technique, overcomes limitations of traditional sampling theorems by enabling efficient compression and transmission of power quality disturbances (PQDs). However, SAMP suffers from issues such as sparsity overestimation, computational inefficiency, and degraded reconstruction accuracy under noisy conditions. To address these challenges, this paper proposes an adaptive threshold SAMP (ATSAMP) algorithm to enhance the performance of PQD reconstruction. First, PQDs are sparsely represented in the discrete Fourier transform (DFT) basis. Then, the reconstruction amplitude difference, which effectively captures amplitude variations, is employed as an iterative termination criterion in the ATSAMP algorithm to improve reconstruction accuracy. The effectiveness of ATSAMP is validated through comparative simulations and experiments against existing methods on typical PQDs. Moreover, this approach meets practical requirements for power quality analysis.