Disturbance extracted methods for auxiliary power quality monitor-based voltage sag localization in distribution network

Abstract

It is of great significance to extract the voltage sag disturbances with high signal-to-noise ratio (SNR) for safety, environmental and economic interests, which can be applied to locate the voltage sag source using the widely deployed auxiliary power quality monitor (αPQM) in distribution network. Two classical disturbance extraction methods (DEM) and the respective advantages, disadvantages and applicable scenarios are summarized. To overcome their shortcomings at the requirements for fault duration and sampling frequency caused by the small storage space of αPQM, three new DEMs with higher SNR are proposed based on the sine wave reconstruction. The extracted disturbances are analyzed in terms of SNR using theoretical signals. Compared with the classical DEMs, the new ones show better SNR performance at sampling frequency, disturbance duration and residual voltage, as well as harmonics and noise. Besides, the test results using the simulation data of the IEEE 34 nodes distribution network show that they outperform classical DEMs in adapting to voltage sag localization algorithms. The sampling frequency of αPQM is recommended to be an integer multiple of the system frequency. In order to adapt αPQM and more DEMs to locate voltage sag, the longest possible steady-state waveforms are suggested to be stored in the case of redundant storage space.