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Found 12 papers, 0 papers with code
Reinforcement Learning Based Symbolic Regression for Load Modeling
With the increasing penetration of renewable energy sources, growing demand variability, and evolving grid control strategies, accurate and efficient load modeling has become a critical yet challenging task.
Mitigating Parameter Degeneracy using Joint Conditional Diffusion Model for WECC Composite Load Model in Power Systems
Data-driven modeling for dynamic systems has gained widespread attention in recent years.
Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators
This paper proposes a new data-driven methodology for predicting intervals of post-fault voltage trajectories in power systems.
Discovery and Characterization of Cross-Area and Intra-Area SSOs Sensitive to Delay in Droop Control of Grid-Forming Converters
Subsynchronous oscillations (SSOs) involving grid-forming converters (GFCs) are in a less familiar territory of power system dynamics.
An Approach to Evaluate Modeling Adequacy for Small-Signal Stability Analysis of IBR-related SSOs in Multimachine Systems
Further, we consider the quasistationary phasor calculus (QPC) framework that neglects transmission line, load, and SG stator dynamics to show its adequacy in SSO modeling and analysis.
Cooperative Multi-Agent Deep Reinforcement Learning for Adaptive Decentralized Emergency Voltage Control
Under voltage load shedding (UVLS) for power grid emergency control builds the last defensive perimeter to prevent cascade outages and blackouts in case of contingencies.
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading
We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19, 461 whole-slide images of prostate cancer from multiple centers.
On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators
This paper designs an Operator Learning framework to approximate the dynamic response of synchronous generators.
DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems
This paper develops a Deep Graph Operator Network (DeepGraphONet) framework that learns to approximate the dynamics of a complex system (e. g. the power grid or traffic) with an underlying sub-graph structure.
DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories
This paper proposes a new data-driven method for the reliable prediction of power system post-fault trajectories.
glassoformer: a query-sparse transformer for post-fault power grid voltage prediction
Due to the sparsified queries, GLassoformer is more computationally efficient than the standard transformers.
Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning
Emergency control, typically such as under-voltage load shedding (UVLS), is broadly used to grapple with low voltage and voltage instability issues in practical power systems under contingencies.
