A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements: Preprint
Conference
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OSTI ID:1813325
Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE); San Diego Gas & Electric, Electric Program Investment Charge (EPIC) Program
- DOE Contract Number:
- AC36-08GO28308;
- OSTI ID:
- 1813325
- Report Number(s):
- NREL/CP-5D00-78299; MainId:32216; UUID:1a82c08c-8c9e-4c3c-b7d9-50ddac16fe3c; MainAdminID:48456
- Conference Information:
- Presented at the 2021 IEEE Power & Energy Society General Meeting, 26-29 July 2021
- Country of Publication:
- United States
- Language:
- English
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