Accurate signal timing from high frequency streaming data

The goal of our study is to analyze massive high-frequency streaming sensor data to accurately locate the source of partial discharges (PD) in transformers. The PD signal is collected by ultra-high frequency sensors at a resolution of 0.4 ns per record resulting in a data streaming rate of 12 GB/s. A voltage threshold is applied to the data stream to extract 400 ns signal samples. We develop a voltage threshold method based on the Savitzky-Golay filter for signal arrival timing, and localize the PD with arrival time differences using Finite-Difference Time-Domain (FDTD) simulation. The Savitzky-Golay filter is able to preserve features better than other methods, resulting in improved signal-to-noise ratios and more accurate signal timing. FDTD accounts for the travel path of signals inside the transformer, allowing for more precise PD localization. Our resulting method localizes PDs more accurately than existing methods, particularly in high noise cases.