235 citations · 635 across the 50 of their papers we have counts for
66 papers
Expediting AC Contingency Analysis using a Basecase Machine Learning Model
Md Obaidur Rahman, Vassilis Kekatos
Grid planning and operation under increasingly variable operating conditions require fast and accurate AC power flow (AC-PF) contingency analysis for numerous line outages. While M…
AC Power Flow Contingency Analysis Using a Single Deep Neural Network
Md Obaidur Rahman, Junjie Qin, Vassilis Kekatos
Contingency analysis using the AC power flow (AC-PF) model is a critical tool for accurate grid security assessment, but its computational burden increases with the number of opera…
Dynamic Modeling of Load Demand in Electrified Highways Based on the EV Composition
Ashutossh Gupta, Vassilis Kekatos, Dionysios Aliprantis +1
Electrified roadways (ERs) equipped with the dynamic wireless power transfer (DWPT) technology can achieve longer driving range and reduce on-board battery requirements for electri…
Interpolatory Approximations of PMU Data: Dimension Reduction and Pilot Selection
Sean Reiter, Mark Embree, Serkan Gugercin +1
This work investigates the reduction of phasor measurement unit (PMU) data through low-rank matrix approximations. To reconstruct a PMU data matrix from fewer measurements, we prop…
Learning AC Power Flow Solutions using a Data-Dependent Variational Quantum Circuit
Thinh Viet Le, Md Obaidur Rahman, Vassilis Kekatos
Interconnection studies require solving numerous instances of the AC load or power flow (AC PF) problem to simulate diverse scenarios as power systems navigate the ongoing energy t…
Frequency-Domain Characterization of Load Demand from Electrified Highways
Ashutossh Gupta, Vassilis Kekatos, Ruoyu Yang +2
Electrified roadways (ER) equipped with dynamic wireless power transfer (DWPT) capabilities can patently extend the driving range and reduce the battery size of electric vehicles (…
Solving Conic Programs over Sparse Graphs using a Variational Quantum Approach: The Case of the AC Optimal Power Flow
Thinh Viet Le, Mark M. Wilde, Vassilis Kekatos
Conic programs arising in physics, quantum information, machine learning, and engineering are often defined over sparse graphs. Although such problems can be solved in polynomial t…
A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks
Zain ul Abdeen, Vassilis Kekatos, Ming Jin
Certified robustness is a critical property for deploying neural networks (NN) in safety-critical applications. A principle approach to achieving such guarantees is to constrain th…
Solving Optimal Power Flow on a Data-Budget: Feature Selection on Smart Meter Data
Vassilis Kekatos, Ridley Annin, Manish K. Singh +1
How much data is needed to optimally schedule distributed energy resources (DERs)? Does the distribution system operator (DSO) have to know load demands at each bus of the feeder t…
Variational Quantum Eigensolver with Constraints (VQEC): Solving Constrained Optimization Problems via VQE
Thinh Viet Le, Vassilis Kekatos
Variational quantum approaches have shown great promise in finding near-optimal solutions to computationally challenging tasks. Nonetheless, enforcing constraints in a disciplined…
Data-driven Forced Oscillation Localization using Inferred Impulse Responses
Shaohui Liu, Hao Zhu, Vassilis Kekatos
Poorly damped oscillations pose threats to the stability and reliability of interconnected power systems. In this work, we propose a comprehensive data-driven framework for inferri…
A Chance-Constrained Optimal Design of Volt/VAR Control Rules for Distributed Energy Resources
Jinlei Wei, Sarthak Gupta, Dionysios C. Aliprantis +1
Deciding setpoints for distributed energy resources (DERs) via local control rules rather than centralized optimization offers significant autonomy. The IEEE Standard 1547 recommen…
A Quantum Approach for Stochastic Constrained Binary Optimization
Sarthak Gupta, Vassilis Kekatos
Analytical and practical evidence indicates the advantage of quantum computing solutions over classical alternatives. Quantum-based heuristics relying on the variational quantum ei…
Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks
Sarthak Gupta, Ali Mehrizi-Sani, Spyros Chatzivasileiadis +1
Volt/VAR control rules facilitate the autonomous operation of distributed energy resources (DER) to regulate voltage in power distribution grids. According to non-incremental contr…
Optimal Design of Volt/VAR Control Rules of Inverters using Deep Learning
Sarthak Gupta, Vassilis Kekatos, Spyros Chatzivasileiadis
Distribution grids are challenged by rapid voltage fluctuations induced by variable power injections from distributed energy resources (DERs). To regulate voltage, the IEEE Standar…
Optimal Design of Volt/VAR Control Rules for Inverter-Interfaced Distributed Energy Resources
Ilgiz Murzakhanov, Sarthak Gupta, Spyros Chatzivasileiadis +1
The IEEE 1547 Standard for the interconnection of distributed energy resources (DERs) to distribution grids provisions that smart inverters could be implementing Volt/VAR control r…
Dynamic Response Recovery Using Ambient Synchrophasor Data: A Synthetic Texas Interconnection Case Study
Shaohui Liu, Hao Zhu, Vassilis Kekatos
Wide-area dynamic studies are of paramount importance to ensure the stability and reliability of power grids. This paper puts forth a comprehensive framework for inferring the dyna…
Learning Distribution Grid Topologies: A Tutorial
Deepjyoti Deka, Vassilis Kekatos, Guido Cavraro
Unveiling feeder topologies from data is of paramount importance to advance situational awareness and proper utilization of smart resources in power distribution grids. This tutori…
Optimal Power Flow Schedules with Reduced Low-Frequency Oscillations
Manish K. Singh, Vassilis Kekatos
The dynamic response of power grids to small events or persistent stochastic disturbances influences their stable operation. Low-frequency inter-area oscillations are of particular…
Learning Neural Networks under Input-Output Specifications
Zain ul Abdeen, He Yin, Vassilis Kekatos +1
In this paper, we examine an important problem of learning neural networks that certifiably meet certain specifications on input-output behaviors. Our strategy is to find an inner…
Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes
Mana Jalali, Manish K. Singh, Vassilis Kekatos +2
Fast inverter control is a desideratum towards the smoother integration of renewables. Adjusting inverter injection setpoints for distributed energy resources can be an effective g…
Data-Driven Modeling of Aggregate Flexibility under Uncertain and Non-Convex Load Models
Sina Taheri, Vassilis Kekatos, Harsha Veeramachaneni +1
Bundling a large number of distributed energy resources through a load aggregator has been advocated as an effective means to integrate such resources into whole-sale energy market…
DNN-based Policies for Stochastic AC OPF
Sarthak Gupta, Sidhant Misra, Deepjyoti Deka +1
A prominent challenge to the safe and optimal operation of the modern power grid arises due to growing uncertainties in loads and renewables. Stochastic optimal power flow (SOPF) f…
Inferring power system dynamics from synchrophasor data using Gaussian processes
Mana Jalali, Vassilis Kekatos, Siddharth Bhela +2
Synchrophasor data provide unprecedented opportunities for inferring power system dynamics, such as estimating voltage angles, frequencies, and accelerations along with power injec…