An Adaptive Synchronized Measurement Method in Distribution Networks Based on Sparse Decomposition Theory

The large-scale integration of renewable energy sources into distribution networks has substantially increased the complexity of power signal characteristics. This complexity makes it challenging to fully capture the dynamic behavior of distribution networks using limited measurement data. To address this, this paper proposes a novel framework for synchronized measurements in distribution networks based on sparse decomposition theory, which can use synchrophasor data with low upload frequency to characterize slow dynamic processes and use small-capacity data to depict distorted waveforms. Given the fluctuating and abrupt changes in phasor and waveform signals within distribution networks, an overcomplete dictionary that combines Ex-Gabor atoms and Damped-sine atoms is designed, which is tailored to effectively decompose both phasor and waveform data. Furthermore, the matching pursuit process is enhanced using a modified sinecosine algorithm. This improvement is achieved by applying nonlinear adjustments to the control parameters and refining the iteration termination conditions, resulting in better decomposition efficiency, improved reconstruction accuracy, and greater resistant to noise interference. Test results show that the proposed method meets the required accuracy standards for subsequent applications.