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Found 6 papers, 0 papers with code
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies.
Grid Impact Analysis and Mitigation of En-Route Charging Stations for Heavy-Duty Electric Vehicles
This paper presents a consolidated grid impact analysis design and corresponding mitigation strategies for heavy-duty electric vehicle (EV) charging stations.
Reinforcement Learning for Volt-Var Control: A Novel Two-stage Progressive Training Strategy
In Stage 1(individual training), while holding all the other agents inactive, we separately train each agent to obtain its own optimal VVC actions in the action space: {consume, generate, do-nothing}.
A Novel Data Segmentation Method for Data-driven Phase Identification
To improve the identification accuracy, a data segmentation method is proposed to exclude data segments that are collected when the voltage correlation between smart meters on the same phase are weakened.
A Data-driven Probabilistic-based Flexibility Region Estimation Method for Aggregated Distributed Energy Resources
At the DER-level, a two-dimensional flexibility region can be formed based on the real and reactive power regulating limits of each DER considering forecast uncertainty.
A Novel Interactive Two-stage Joint Retail Electricity Market for Multiple Microgrids
To accommodate the advent of microgrids (MG) managing distributed energy resources (DER) in distribution systems, an interactive two-stage joint retail electricity market mechanism is proposed to provide an effective platform for these prosumers to proactively join in retail transactions.
