A High Efficient Approach for Power Disturbance Waveform Compression in the View of Heisenberg Uncertainty

This paper proposes a highly efficient approach for power disturbance waveform (PDW) compression in the view of Heisenberg uncertainty. The key idea is to represent each signal component of PDW using as few nonzero coefficients as possible by the uncertainty principle restriction. PDWs are projected in a union of bases (UB), and each signal component of the PDWs can be represented very sparsely. The UB decomposition is solved by orthogonal matching pursuit. The features and cross correlation of subbases of the UB guarantee the PDW compression with high a compression ratio and recovered accuracy. With various simulated and field PDWs tests, the compressed data size of the new method is proven with good characteristics such as low sensitivity to sampling frequency increment and types of signal components contained in PDWs. Moreover, it is found that the new method and methods that employ wavelet techniques share the similar effect of noise for PDW compression. The proposed method is also applied at a 220-kV power substation for field PDW compression from fault recorders. The comparisons, analyses, and experiments indicate that the proposed method has a high PDW compression efficiency for future power grid monitoring.