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Found 8 papers, 0 papers with code
Decentralized Coordinated State Estimation in Integrated Transmission and Distribution Systems
Current transmission and distribution system states are mostly unobservable to each other, and state estimation is separately conducted in the two systems owing to the differences in network structures and analytical models.
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.
Evaluating Load Models and Their Impacts on Power Transfer Limits
Since the load dynamics have substantial impacts on power system transient stability, load models are one critical factor that affects the power transfer limits.
Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach
However, a detailed WECC CLM model typically has a high degree of complexity, with over one hundred parameters, and no systematic approach to identifying and calibrating these parameters.
Deep Generative Graph Distribution Learning for Synthetic Power Grids
Power system studies require the topological structures of real-world power networks; however, such data is confidential due to important security concerns.
Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting
In this paper, a deep generative model is introduced to capture continuous probability densities corresponding to the nodes of an arbitrary graph.
Energy Disaggregation via Deep Temporal Dictionary Learning
The electricity signal of each device is then modeled by a linear combination of such patterns with sparse coefficients that determine the contribution of each device in the total electricity.
A Multi-model Combination Approach for Probabilistic Wind Power Forecasting
Therefore, a novel multi-model combination (MMC) approach for short-term probabilistic wind generation forecasting is proposed in this paper to exploit the advantages of different forecasting models.
