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Found 13 papers, 0 papers with code
Efficient Probabilistic Optimal Power Flow Assessment Using an Adaptive Stochastic Spectral Embedding Surrogate Model
This paper presents an adaptive stochastic spectral embedding (ASSE) method to solve the probabilistic AC optimal power flow (AC-OPF), a critical aspect of power system operation.
Modeling False Data Injection Attacks on Integrated Electricity-Gas Systems
Next, we develop FDIAs on IEGSs when intruders have only local network topology and parameter information of an IEGS.
On Data-Driven Modeling and Control in Modern Power Grids Stability: Survey and Perspective
Modern power grids are fast evolving with the increasing volatile renewable generation, distributed energy resources (DERs) and time-varying operating conditions.
A Comparative Study of Polynomial Chaos Expansion-Based Methods for Global Sensitivity Analysis in Power System Uncertainty Control
In contrast, the PCE model built using correlated random inputs directly yields the most accurate ANCOVA indices for global sensitivity analysis.
A Data-Driven Polynomial Chaos Expansion-Based Method for Microgrid Ramping Support Capability Assessment and Enhancement
Microgrids (MGs) are regarded as effective solutions to provide ramping support to the main grid during heavy-load periods.
An Online Data-Driven Method to Locate Forced Oscillation Sources from Power Plants Based on Sparse Identification of Nonlinear Dynamics (SINDy)
Forced oscillations may jeopardize the secure operation of power systems.
An Online Data-Driven Method for Microgrid Secondary Voltage and Frequency Control with Ensemble Koopman Modeling
Low inertia, nonlinearity and a high level of uncertainty (varying topologies and operating conditions) pose challenges to microgrid (MG) systemwide operation.
A Sparse Polynomial Chaos Expansion-Based Method for Probabilistic Transient Stability Assessment and Enhancement
This paper proposes an adaptive sparse polynomial chaos expansion(PCE)-based method to quantify the impacts of uncertainties on critical clearing time (CCT) that is an important index in transient stability analysis.
A Data-Driven Uncertainty Quantification Method for Stochastic Economic Dispatch
This letter proposes a data-driven sparse polynomial chaos expansion-based surrogate model for the stochastic economic dispatch problem considering uncertainty from wind power.
Targeted False Data Injection Attacks Against AC State Estimation Without Network Parameters
State estimation is a data processing algorithm for converting redundant meter measurements and other information into an estimate of the state of a power system.
Wide-Area Damping Control for Interarea Oscillations in Power Grids Based on PMU Measurements
In this paper, a phasor measurement unit (PMU)-based wide-area damping control method is proposed to damp the interarea oscillations that threaten the modern power system stability and security.
A Data-Driven Sparse Polynomial Chaos Expansion Method to Assess Probabilistic Total Transfer Capability for Power Systems with Renewables
The increasing uncertainty level caused by growing renewable energy sources (RES) and aging transmission networks poses a great challenge in the assessment of total transfer capability (TTC) and available transfer capability (ATC).
Measurement-Based Estimation of System State Matrix for AC Power Systems with Integrated VSCs
Numerical studies in the IEEE 68-bus system with integrated VSCs show that the proposed measurementbased method can accurately identify the electromechanical modes and estimate the damping ratios, the mode shapes, and the participation factors.
