This guide provides an overview of new and existing methods to extract meaningful information from power quality (PQ) data using data analytics. PQ data may include value logs (time series), waveforms (point on wave), phasors, spectrums, and characteristics recorded during PQ disturbances such as voltage sags, voltage swells, transients, rapid voltage change events, and metadata related to PQ data. Analytic techniques such as feature engineering, transformation, and machine learning are presented. Pre-processing of data includes methods for data collection, wrangling, structuring, and data quality. Effective methods of presentation and reporting of data are covered, including charting, geospatial, and other visualizations. Case studies are provided demonstrating examples of PQ data analytics. Roles of personnel managing PQ data analytics are included. These can include PQ subject matter experts, data engineers, data analysts, data scientist, and end-users/stakeholders.
Guide for Power Quality Data Analytics
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