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.
- Sponsor Committee
- PE/T&D - Transmission and Distribution
Learn More About PE/T&D - Transmission and Distribution - Status
- Active PAR
- PAR Approval
- 2021-12-08
Working Group Details
- Society
- IEEE Power and Energy Society
Learn More About IEEE Power and Energy Society - Sponsor Committee
- PE/T&D - Transmission and Distribution
Learn More About PE/T&D - Transmission and Distribution - Working Group
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PQAD - Working Group on Power Quality Data Analytics
Learn More About PQAD - Working Group on Power Quality Data Analytics - IEEE Program Manager
- Michael Kipness
Contact Michael Kipness - Working Group Chair
- Kevin Kittredge
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