| Abstract: | Wavelet transform is usually used to deal with the numerous data in fault recording of power systems, but it still needs to sample the signal with a high frequency first and then compress the data obtained. In order to solve the problem, this paper proposes a new fault recording method based on compressed sensing theory. The method can break the limit of Nyquist sampling theorem and acquire the fault signal with a much lower sampling frequency. It presents the theoretical framework of compressed sensing, analyzes the characteristics of the faulty waveform, and proposes a new sparsity adaptive and compressive sampling matching pursuit algorithm to reconstruct the fault signal. Finally, simulation results in MATLAB show that the proposed method performs well at different compression ratios in terms of the norm mean‐square error, signal‐to‐noise ratio, and waveform similarity. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. |
