- Power system maintenance
- Grid reliability monitoring
- More effectively addresses signal background noise
- More accurately locates potential transformer failures
Researchers at Berkeley Lab have developed a technology to locate the position of partial discharge events in the power grid based on the information from a set of ultra-high frequency (UHF) sensors from the transformer. Monitoring for partial discharge events helps ensure power grid reliability, as these events are a symptom of insulation weakness and the most common cause of transformer failure.
The Berkeley Lab technology determines the signal arrival time using a convolutional filtering method to reduce the impact of background noise. Time difference of arrival reveals the partial discharge source. In tests using two sets of UHF measurements with different signal to noise ratios, the Berkeley Lab technology provided more accurate locations than existing methods, particularly when signals were weak. With weak signals, the best existing method predicted the location within 300 mm in 13% of the test cases compared to 48% of the test cases for the Berkeley Lab approach.
DEVELOPMENT STAGE: See researcher test results in the IEEE Computer Society publication linked below.
FOR MORE INFORMATION:
Wang, J., Wu, K., Sim, A., Hwangbo, S. “Convolutional filtering for accurate signal timing from noisy streaming data,” IEEE Computer Society, 2017. DOI 10.1109/DASC-PICom-DataCom-CyberSciTec.2017.157
STATUS: Published U. S. Patent Application #16/182,519 (Publication No. 2019/0138371). Available for licensing or collaborative research.
REFERENCE NUMBER: 2017-111