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- Title
Non-Parametric Statistical Analysis of Current Waveforms through Power System Sensors †.
- Authors
Wilson, Aaron J.; Warmack, Bruce R. J.; Ekti, Ali Riza; Liu, Yilu
- Abstract
The protection, control, and monitoring of the power grid is not possible without accurate measurement devices. As the percentage of renewable energy sources penetrating the existing grid infrastructure increases, so do uncertainties surrounding their effects on the everyday operation of the power system. Many of these devices are sources of high-frequency transients. These transients may be useful for identifying certain events or behaviors otherwise not seen in traditional analysis techniques. Therefore, the ability of sensors to accurately capture these phenomena is paramount. In this work, two commercial-grade power system distribution sensors are investigated in terms of their ability to replicate high-frequency phenomena by studying their responses to three events: a current inrush, a microgrid "close-in", and a fault on the terminals of a wind turbine. Kernel density estimation is used to derive the non-parametric probability density functions of these error distributions and their adequateness is quantified utilizing the commonly used root mean square error (RMSE) metric. It is demonstrated that both sensors exhibit characteristics in the high harmonic range that go against the assumption that measurement error is normally distributed.
- Subjects
WAVE analysis; STANDARD deviations; PROBABILITY density function; STATISTICS; MICROGRIDS; RENEWABLE energy sources; PHASOR measurement
- Publication
Sensors (14248220), 2022, Vol 22, Issue 22, p8827
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s22228827