@INPROCEEDINGS{talkington_recovering_pf_2021,
author={Talkington, Samuel and Grijalva, Santiago and Reno, Matthew J. and Azzolini, Joseph},
booktitle={2021 North American Power Symposium (NAPS)},
title={Recovering Power Factor Control Settings of Solar PV Inverters from Net Load Data},
year={2021},
volume={},
number={},
pages={1-6},
keywords={Reactive power;Sensitivity;Filtering;Linear regression;Estimation;Data aggregation;Inverters;net load reactive power disaggregation;voltage regulation;reactive power control;advanced inverters;data-driven engineering},
doi={10.1109/NAPS52732.2021.9654671}
}Advanced solar PV inverter control settings may not be reported to utilities or may be changed without notice. This paper develops an estimation method for determining a fixed power factor control setting of a behind-the-meter (BTM) solar PV smart inverter. The estimation is achieved using linear regression methods with historical net load advanced metering infrastructure (AMI) data. Notably, the BTM PV power factor setting may be unknown or uncertain to a distribution engineer, and cannot be trivially estimated from the historical AMI data due to the influence of the native load on the measurements. To solve this, we use a simple percentile-based approach for filtering the measurements. A physics-based linear sensitivity model is then used to determine the fixed power factor control setting from the sensitivity in the complex power plane. This sensitivity parameter characterizes the control setting hidden in the aggregate data. We compare several loss functions, and verify the models developed by conducting experiments on 250 datasets based on real smart meter data. The data are augmented with synthetic quasi-static-timeseries (QSTS) simulations of BTM PV that simulate utility-observed aggregate measurements at the load. The simulations demonstrate the reactive power sensitivity of a BTM PV smart inverter can be recovered efficiently from the net load data after applying the filtering approach.
Error Bounds for Radial Network Topology Learning from Quantized Measurements
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arXiv / bibTeX
VArsity: Can Large Language Models Keep Power Engineering Students in Phase?
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57th North American Power Symposium (NAPS 2025) / arXiv / bibTeX
Covert Distribution Load Tripping Attacks
Betelihem Kebede Ashebo, Samuel Talkington, Saman Zonouz, and Daniel K. Molzahn
57th North American Power Symposium (NAPS 2025) / code / bibTeX
Classifying Reactive Power Control Laws of Behind-the-Meter Solar Photovoltaic Inverters
Richard Asiamah, Samuel Talkington, Michael Boateng, Marta Vanin, Frederik Geth, and Daniel K. Molzahn
Kansas Power and Energy Conference (KPEC) / bibTeX
Strategic Electric Distribution Network Sensing via Spectral Bandits
Samuel Talkington, Rahul Gupta, Richard Asiamah, Paprapee Buason, and Daniel K. Molzahn
IEEE CDC 2024 / arXiv / slides / bibTeX