Overview of Big Data Analytics in Power Quality Analysis and Assessment - Siqueira-de-Carvalho, Ricardo; Morales-Paredes, Helmo K. [UNESP]; Bates, Carson; Ausmus, Jason; Simões, Marcelo G.; Sen, Pankaj K. (Springer Nature, 2024)
The legacy electric grid is changing at a fast pace and is expected to have high penetration levels of distributed energy resources (DER) and non-linear loads at the distribution level. This introduces several challenges to the monitoring and control of the distribution network. One challenge is to address the significant increase of higher-order harmonics (h) in voltage and current waveforms. At present, power quality (PQ) meters have sampling rate limitations and cannot detect higher-order harmonics. The amount of available data in the electric grid is also increasing at an exponential rate and has grown to big data size. Big Data Analytics (BDA) may provide several opportunities for the monitoring and control of voltage and current harmonics. The effects of increasing the sampling rate for monitoring higher-order harmonics are discussed, and this paper explores new ideas on BDA for modern distribution systems operation, specifically, for Power Quality (PQ) analysis and assessment.
Keywords: Big Data Analytics; Distributed Energy Resources; Power Distribution; Power Quality; Renewable Energy; Big data analytic; Data analytics; High order harmonics; Higher order harmonics; Power; Power distributions; Power quality assessment; Power-quality analysis; Renewable energies