Abstract
Compressed sensing technology breaks through the limitation of Nyquist sampling theorem, and can effectively reduce the cost of data storage and transmission. Only a few samples are needed to reconstruct the power quality signal by using the compressed sensing technology, which is of great significance for the detection and analysis of power quality. We propose an algorithm based on multitask Bayesian compressed sensing (MT-BCS) theory for power quality signals compression and reconstruction in this paper. The power quality signals are changed to sparse vectors by taking the fast Fourier transform basis as the sparse basis. The real and imaginary parts of sparse vectors are then treated as two compression and reconstruction tasks. Considering the internal correlation of data corresponding to these two tasks, the power quality signal is reconstructed using the sharing mechanism of hyperparameter estimation. The simulation results show that the algorithm is superior to the orthogonal matching pursuit algorithm and the Bayesian compressed sensing algorithm in anti-noise performance and reconstruction accuracy, and is more suitable for compressing and reconstructing power quality signals with complex disturbance.
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