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Found 16 papers, 0 papers with code
Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee
The results show that we can detect and localize the outage in a timely manner with only voltage magnitudes and without assuming a prior knowledge of outage patterns.
Digital twins of nonlinear dynamical systems
We articulate the design imperatives for machine-learning based digital twins for nonlinear dynamical systems subject to external driving, which can be used to monitor the ``health'' of the target system and anticipate its future collapse.
CoNSoLe: Convex Neural Symbolic Learning
In the first step of searching for right symbols, we convexify the deep Q-learning.
Explainable Graph Theory-Based Identification of Meter-Transformer Mapping
Distributed energy resources are better for the environment but may cause transformer overload in distribution grids, calling for recovering meter-transformer mapping to provide situational awareness, i. e., the transformer loading.
Local Measurement Based Robust Voltage Stability Index & Identification of Voltage Collapse Onset
When compared to existing methods, we observe that LD-VSI is not only more robust to measurement noise but also can identify VCP.
Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading
To address the challenge of the large optimization space, a curriculum-based approach with reward tuning is incorporated into the training procedure by modifying the environment using network physics for enhanced agent learning.
The Powerful Use of AI in the Energy Sector: Intelligent Forecasting
To meet society requirements, this paper proposes a methodology to develop, deploy, and evaluate AI systems in the energy sector by: (1) understanding the power system measurements with physics, (2) designing AI algorithms to forecast the need, (3) developing robust and accountable AI methods, and (4) creating reliable measures to evaluate the performance of the AI model.
Dataset transformations trade-offs to adapt machine learning methods across domains
To prove our point, we examine dataset transformations used in the literature to adapt machine learning-based methods across domains and show that these dataset transformations are not always beneficial in terms of performance.
Where can quantum kernel methods make a big difference?
In this paper, by exploring and summarizing the essential differences between quantum kernel functions and classical kernel functions, we propose a criterion based on inter-class and intra-class distance and geometric properties to determine under what circumstances quantum kernel methods will be superior.
WHAT TO DO IF SPARSE REPRESENTATION LEARNING FAILS UNEXPECTEDLY?
RISE is based on a complete analysis for the generalizability of data properties for physical systems.
Adversarial twin neural networks: maximizing physics recovery for physical system
Thus, we propose (1) sparsity regularization for the physical model and (2) physical superiority over the virtual model.
Quick Line Outage Identification in Urban Distribution Grids via Smart Meters
The growing integration of distributed energy resources (DERs) in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors.
Fast Distribution Grid Line Outage Identification with $μ$PMU
This makes the theory on optimal change-point detection suitable to identify line outages via $\mu$PMUs with fast and accurate sampling.
Unbalanced Multi-Phase Distribution Grid Topology Estimation and Bus Phase Identification
Then, this paper proves that the Chow-Liu algorithm finds the topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi-phase structure of distribution grids with the presence of incorrect bus phase labels.
Urban MV and LV Distribution Grid Topology Estimation via Group Lasso
The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution grid.
A Sparse Linear Model and Significance Test for Individual Consumption Prediction
Accurate prediction of user consumption is a key part not only in understanding consumer flexibility and behavior patterns, but in the design of robust and efficient energy saving programs as well.
