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

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.

Authors:
 [1];  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1417802
Report Number(s):
NREL/CP-5D00-68043
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 19th International Conference on Intelligent System Application to Power Systems (ISAP), 17-20 September 2017, San Antonio, Texas
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; variability load modeling; quasi-static time series (QSTS) studies; discrete wavelet transforms (DWT); data mining

Citation Formats

Chamana, Manohar, and Mather, Barry A. Variability Extraction and Synthesis via Multi-Resolution Analysis using Distribution Transformer High-Speed Power Data. United States: N. p., 2017. Web. doi:10.1109/ISAP.2017.8071389.
Chamana, Manohar, & Mather, Barry A. Variability Extraction and Synthesis via Multi-Resolution Analysis using Distribution Transformer High-Speed Power Data. United States. doi:10.1109/ISAP.2017.8071389.
Chamana, Manohar, and Mather, Barry A. Thu . "Variability Extraction and Synthesis via Multi-Resolution Analysis using Distribution Transformer High-Speed Power Data". United States. doi:10.1109/ISAP.2017.8071389.
@article{osti_1417802,
title = {Variability Extraction and Synthesis via Multi-Resolution Analysis using Distribution Transformer High-Speed Power Data},
author = {Chamana, Manohar and Mather, Barry A},
abstractNote = {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.},
doi = {10.1109/ISAP.2017.8071389},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Oct 19 00:00:00 EDT 2017},
month = {Thu Oct 19 00:00:00 EDT 2017}
}

Conference:
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