Application of staggered undersampling to power quality monitoring

In this paper we address the issue of monitoring power quality with limited sensing and computational resources. The proposed staggered sampling methodology utilizes close-to-periodic nature of waveforms of interest. The sparse samples obtained in our scheme are reordered (shuffled) before FFT is used for spectral calculations. We provide a complete characterization of the shuffling process in time domain and of the dual inverse shuffling in the frequency domain. The methodology is applied to a number of cases of interest in power quality, demonstrating that harmonics of the fundamental frequency can be recovered without error, and that effective descriptions are possible for flicker, sag, interharmonics, and noisy measurements.