Variability Extraction and Synthesis via Multi-Resolution Analysis using Distribution Transformer High-Speed Power Data

Manohar Chamana, Barry Mather

Research output: Contribution to conferencePaperpeer-review

6 Scopus Citations

Abstract

A library of load variability classes is created to produce scalable synthetic data sets using historical high-speed raw data. These data are collected from distribution monitoring units connected at the secondary side of a distribution transformer. Because of the irregular patterns and large volume of historical high-speed data sets, the utilization of current load characterization and modeling techniques are challenging. Multi-resolution analysis techniques are applied to extract the necessary components and eliminate the unnecessary components from the historical high-speed raw data to create the library of classes, which are then utilized to create new synthetic load data sets. A validation is performed to ensure that the synthesized data sets contain the same variability characteristics as the training data sets. The synthesized data sets are intended to be utilized in quasi-static time-series studies for distribution system planning studies on a granular scale, such as detailed PV interconnection studies.

Original languageAmerican English
Number of pages6
DOIs
StatePublished - 18 Oct 2017
Event19th International Conference on Intelligent System Application to Power Systems, ISAP 2017 - San Antonio, United States
Duration: 17 Sep 201720 Sep 2017

Conference

Conference19th International Conference on Intelligent System Application to Power Systems, ISAP 2017
Country/TerritoryUnited States
CitySan Antonio
Period17/09/1720/09/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

NREL Publication Number

  • NREL/CP-5D00-68043

Keywords

  • data mining
  • discrete wavelet transforms
  • DWT
  • QSTS
  • quasi-static time series
  • Variability load modeling

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