2 citations · 4 across the 18 of their papers we have counts for
18 papers
Homotopy-Guided Self-Supervised Learning of Parametric Solutions for AC Optimal Power Flow
Shimiao Li, Aaron Tuor, Draguna Vrabie +2
Learning to optimize (L2O) parametric approximations of AC optimal power flow (AC-OPF) solutions offers the potential for fast, reusable decision-making in real-time power system o…
Voltage-Regulated Sparse Optimization for Proactive Diagnosis of Voltage Collapses
Qinghua Ma, Seyyedali Hosseinalipour, Ming Shi +2
This paper aims to proactively diagnose and manage the voltage collapse risks, i.e., the risk of bus voltages violating the safe operational bounds, which can be caused by extreme…
Frequency-Aware Sparse Optimization for Diagnosing Grid Instabilities and Collapses
Swadesh Vhakta, Denis Osipov, Reetam Sen Biswas +3
This paper aims to proactively diagnose and manage frequency instability risks from a steady-state perspective, without the need for derivative-dependent transient modeling. Specif…
Sparsity-exploiting Gaussian Process for Robust Transient Learning of Power System Dynamics
Tina Gao, Shimiao Li, Lawrence Pileggi
Advances in leveraging Gaussian processes (GP) have enabled learning and inferring dynamic grid behavior from scarce PMU measurements. However, real measurements can be corrupted b…
Cyber-Resilient System Identification for Power Grid through Bayesian Integration
Shimiao Li, Guannan Qu, Bryan Hooi +3
Power grids increasingly need real-time situational awareness under the ever-evolving cyberthreat landscape. Advances in snapshot-based system identification approaches have enable…
Multi-Period Sparse Optimization for Proactive Grid Blackout Diagnosis
Qinghua Ma, Reetam Sen Biswas, Denis Osipov +3
Existing or planned power grids need to evaluate survivability under extreme events, like a number of peak load overloading conditions, which could possibly cause system collapses…
Synergies between Federated Foundation Models and Smart Power Grids
Seyyedali Hosseinalipour, Shimiao Li, Adedoyin Inaolaji +3
The recent emergence of large language models (LLMs) such as GPT-3 has marked a significant paradigm shift in machine learning. Trained on massive corpora of data, these models dem…
Exploiting sparse structures and synergy designs to advance situational awareness of electrical power grid
Shimiao Li
The growing threats of uncertainties, anomalies, and cyberattacks on power grids are driving a critical need to advance situational awareness which allows system operators to form…
Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning
Shimiao Li, Jan Drgona, Shrirang Abhyankar +1
Recent years have seen a rich literature of data-driven approaches designed for power grid applications. However, insufficient consideration of domain knowledge can impose a high r…
Contingency Analyses with Warm Starter using Probabilistic Graphical Model
Shimiao Li, Amritanshu Pandey, Larry Pileggi
Cyberthreats are an increasingly common risk to the power grid and can thwart secure grid operations. We propose to extend contingency analysis to include cyberthreat evaluations.…
Towards Practical Physics-Informed ML Design and Evaluation for Power Grid
Shimiao Li, Amritanshu Pandey, Larry Pileggi
When applied to a real-world safety critical system like the power grid, general machine learning methods suffer from expensive training, non-physical solutions, and limited interp…
Circuit-theoretic Line Outage Distribution Factor
Shimiao Li, Amritanshu Pandey, Larry Pileggi
This work presents the design of AC line outage distribution factor created from the circuit-theoretic power flow models. Experiment results are shown to demonstrate its efficacy i…
A Convex Method of Generalized State Estimation using Circuit-theoretic Node-breaker Model
Shimiao Li, Amritanshu Pandey, Larry Pileggi
An accurate and up-to-date topology is critical for situational awareness of a power grid; however, wrong switch statuses due to physical damage, communication error, or cyber-atta…
Combined Transmission and Distribution State-Estimation for Future Electric Grids
Amritanshu Pandey, Shimiao Li, Larry Pileggi
Proliferation of grid resources on the distribution network along with the inability to forecast them accurately will render the existing methodology of grid operation and control…
Dynamic Graph-Based Anomaly Detection in the Electrical Grid
Shimiao Li, Amritanshu Pandey, Bryan Hooi +2
Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs? A key part of achieving this goal is to use the network of power grid sensor…
A WLAV-based Robust Hybrid State Estimation using Circuit-theoretic Approach
Shimiao Li, Amritanshu Pandey, Larry Pileggi
For reliable and secure power grid operation, AC state-estimation (ACSE) must provide certain guarantees of convergence while being resilient against bad-data. This paper develops…
A LASSO-Inspired Approach for Localizing Power System Infeasibility
Shimiao Li, Amritanshu Pandey, Aayushya Agarwal +2
A method is proposed to identify and localize the cause of network collapse with augmented power flow analysis for a grid model with insufficient resources. Owing to heavy network…
A Circuit-Theoretic Approach to State Estimation
Shimiao Li, Amritanshu Pandey, Soummya Kar +1
Traditional state estimation (SE) methods that are based on nonlinear minimization of the sum of localized measurement error functionals are known to suffer from non-convergence an…