Variability Extraction and Synthesis via Multi-Resolution Analysis using Distribution Transformer High-Speed Power Data
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
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.
- Research Organization:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1417802
- Report Number(s):
- NREL/CP-5D00-68043
- 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
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