Wang, J.; Wu, K.; Sim, A.; Hwangbo, S. Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering. Sensors2023, 23, 1789.
Wang, J.; Wu, K.; Sim, A.; Hwangbo, S. Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering. Sensors 2023, 23, 1789.
Wang, J.; Wu, K.; Sim, A.; Hwangbo, S. Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering. Sensors2023, 23, 1789.
Wang, J.; Wu, K.; Sim, A.; Hwangbo, S. Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering. Sensors 2023, 23, 1789.
Abstract
The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating positons of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determine the signal arrival time and then locate the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transfers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.
Keywords
Partial discharges; Source location; UHF measurements; Time of arrival estimation; Waveform analysis; FDTD methods; Nonlinear wave propagation
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
