This paper was led by my undergraduate mentees Jeslyn Ero, Kieran Slattery, and Xianhe (Ken) Qin as part of the Georgia Tech Opportunity Research Scholars' Undergraduate Research Program.
@INPROCEEDINGS{ero2025optimizingstateestimation,
author={Ero, Jeslyn and Slattery, Kieran and Qin, Xianhe and Talkington, Samuel and Molzahn, Daniel K.},
booktitle={2025 IEEE Opportunity Research Scholars Symposium (ORSS)},
title={{Optimizing State Estimation Error with the LinDist3Flow Model}},
year={2025},
volume={},
number={},
pages={1-4},
doi={10.1109/ORSS66051.2025.11121639}}This research paper presents an algorithmic approach to optimizing state estimation errors in unbalanced distribution networks as the integration of renewable energy sources such as solar and wind increases. These resources introduce uncertainty into grid models, challenging the satisfaction of engineering constraints.The study focuses on the impacts of this randomness on power flow equations and aims to enhance grid state estimation by combining electric grid physics with techniques from probability theory. The goal is to develop methodologies that effectively utilize uncertain measurement data, optimizing the accuracy and efficiency of smart meter data streams.The algorithm implementation aims to learn the optimal line parameters (resistance and reactance) in a three-phase unbalanced distribution system by minimizing the discrepancy between predicted and measured voltage values. It leverages the LinDist3Flow power flow approximation model to simulate voltage magnitudes, and formulates a loss function representing the squared error between the predicted and noisy measurements. The ADAM optimization algorithm iteratively minimizes this loss function, adjusting the parameters to achieve the best fit to observed data. This contributes to improving the accuracy of power flow modeling under noisy and unbalanced conditions.
Efficient Network Reconfiguration by Randomized Switching
Samuel Talkington, Dmitrii M. Ostrovskii, and Daniel K. Molzahn
arXiv / code / bibTeX
Admittance Matrix Concentration Inequalities for Understanding Uncertain Power Networks
Samuel Talkington, Cameron Khanpour, Rahul K. Gupta, Sergio A. Dorado-Rojas, Daniel Turizo, Hyeongon Park, Dmitrii M. Ostrovskii, and Daniel K. Molzahn
arXiv / bibTeX
Differentiating Through Power Flow Solutions for Admittance and Topology Control
Samuel Talkington, Daniel Turizo, Sergio A. Dorado-Rojas, Rahul K. Gupta, and Daniel K. Molzahn
arXiv / bibTeX
Optimizing State Estimation Error in the LinDist3Flow Model
Jeslyn Ero, Kieran Slattery, Xianhe Qin, Samuel Talkington, and Daniel K. Molzahn
IEEE Opportunity Research Scholars Symposium (ORSS) / bibTeX
Error Bounds for Radial Network Topology Learning from Quantized Measurements
Samuel Talkington, Aditya Rangarajan, Pedro A. de Alcântara, Line Roald, Daniel K. Molzahn, and Daniel R. Fuhrmann
arXiv / bibTeX
VArsity: Can Large Language Models Keep Power Engineering Students in Phase?
Samuel Talkington and Daniel K. Molzahn
57th North American Power Symposium (NAPS 2025) / 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
A data-driven sensor placement approach for detecting voltage violations in distribution systems
Paprapee Buason, Sidhant Misra, Samuel Talkington, and Daniel K. Molzahn
Electric Power Systems Research / arXiv / bibTeX
Managing Vehicle Charging During Emergencies via Conservative Distribution System Modeling
Alejandro D. Owen Aquino, Samuel Talkington, and Daniel K. Molzahn
2024 IEEE Texas Power and Energy Conference (TPEC) / arXiv / bibTeX
Localized Structure in Secondary Distribution System Voltage Sensitivity Matrices
Samuel Talkington, Santiago Grijalva, Matthew J. Reno, Joseph A. Azzolini, and Jouni Peppanen
Electric Power Systems Research / bibTeX
A measurement-based approach to voltage-constrained hosting capacity analysis with controllable reactive power behind-the-meter
Samuel Talkington, Santiago Grijalva, Matthew J. Reno, Joseph A. Azzolini, David Pinney
Electric Power Systems Research / bibTeX
Phase Retrieval via Model-Free Power Flow Jacobian Recovery
Samuel Talkington, Santiago Grijalva
ACM e-Energy / arXiv / code / slides / bibTeX
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power Injections
Samuel Talkington, Daniel Turizo, Santiago Grijalva, Jorge Fernandez, Daniel K. Molzahn
IEEE Transactions on Power Systems / arXiv / code / bibTeX
Calculating PV Hosting Capacity in Low-Voltage Secondary Networks Using Only Smart Meter Data
Joseph A. Azzolini, Matthew J. Reno, Jubair Yusuf, Samuel Talkington, Santiago Grijalva
IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) / code / bibTeX
Improving Behind-the-Meter PV Impact Studies with Data-Driven Modeling and Analysis
Joseph A. Azzolini, Samuel Talkington, Matthew J. Reno, Santiago Grijalva, Logan Blakely, David Pinney
IEEE 49th Photovoltaics Specialists Conference (PVSC) / code / bibTeX
Sparse Time-Series Sampling for Recovery of Behind-the-Meter Inverter Control Models
Samuel Talkington, Santiago Grijalva, and Matthew J. Reno
IEEE PES Innovative Smart Grid Technologies Conference (ISGT) / bibTeX
Solar PV Inverter Reactive Power Disaggregation and Control Setting Estimation
Samuel Talkington, Santiago Grijalva, Matthew J. Reno, Joseph A. Azzolini
IEEE Transactions on Power Systems / bibTeX
Recovering Power Factor Control Settings of Solar PV Inverters from Net Load Data
Samuel Talkington, Santiago Grijalva, Matthew J. Reno, Joseph A. Azzolini
North American Power Symposium (NAPS) / slides / bibTeX
Power Factor Estimation of Distributed Energy Resources Using Voltage Magnitude Measurements
Samuel Talkington, Santiago Grijalva, and Matthew J. Reno
Journal of Modern Power Systems and Clean Energy / bibTeX
Rail Transit Regenerative Braking Energy Recovery Optimization to Provide Grid Services
Samuel Talkington, Santiago Grijalva
IEEE Power and Energy Conference at Illinois (PECI) / bibTeX