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Found 10 papers, 1 papers with code
Power Grid Transient Analysis via Open-Source Circuit Simulator: A Case Study of HVDC
This paper proposes an electronic circuit simulator-based method to accelerate the power system transient simulation, where the modeling of a generic HVDC (High Voltage Direct Current) system is focused.
MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids
Integrating variable renewable energy into the grid has posed challenges to system operators in achieving optimal trade-offs among energy availability, cost affordability, and pollution controllability.
A Free Industry-grade Education Tool for Bulk Power System Reliability Assessment
A free industry-grade education tool is developed for bulk-power-system reliability assessment.
End-to-End Topology-Aware Machine Learning for Power System Reliability Assessment
Conventional power system reliability suffers from the long run time of Monte Carlo simulation and the dimension-curse of analytic enumeration methods.
Power Grid Cascading Failure Mitigation by Reinforcement Learning
This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL).
A Two-Level Simulation-Assisted Sequential Distribution System Restoration Model With Frequency Dynamics Constraints
This paper proposes a service restoration model for unbalanced distribution systems and inverter-dominated microgrids (MGs), in which frequency dynamics constraints are developed to optimize the amount of load restoration and guarantee the dynamic performance of system frequency response during the restoration process.
Mitigating Multi-Stage Cascading Failure by Reinforcement Learning
This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL) method.
Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification
This paper presents a study on power grid disturbance classification by Deep Learning (DL).
Power Market Price Forecasting via Deep Learning
Then the raw input and output data are preprocessed by unit scaling, and the trained network is tested on the real price data under different input lengths, forecasting horizons and data sizes.
Adversarial Feature Learning of Online Monitoring Data for Operational Risk Assessment in Distribution Networks
(2) operational risk assessment.
