Spatiotemporal deep learning for power system applications: a survey
M Saffari, M Khodayar - IEEE Access, 2024 - ieeexplore.ieee.org
Understanding spatiotemporal correlations in power systems is crucial for maintaining grid
stability, reliability, and efficiency. By discerning connections between spatial and temporal …
stability, reliability, and efficiency. By discerning connections between spatial and temporal …
Physics-informed graphical neural network for power system state estimation
State estimation is highly critical for accurately observing the dynamic behavior of the power
grids and minimizing risks from cyber threats. However, existing state estimation methods …
grids and minimizing risks from cyber threats. However, existing state estimation methods …
Complex-value spatio-temporal graph convolutional neural networks and its applications to electric power systems AI
The effective representation, processing, analysis, and visualization of large-scale structured
data over graphs, especially power grids, are gaining a lot of attention. So far most of the …
data over graphs, especially power grids, are gaining a lot of attention. So far most of the …
Multi-graph attention fusion graph neural network for remaining useful life prediction of rolling bearings
Y Xiao, L Cui, D Liu - Measurement Science and Technology, 2024 - iopscience.iop.org
Graph neural network (GNN) has the proven ability to learn feature representations from
graph data, and has been utilized for the tasks of predicting the machinery remaining useful …
graph data, and has been utilized for the tasks of predicting the machinery remaining useful …
A heterogeneous graph-based multi-task learning for fault event diagnosis in smart grid
D Chanda, NY Soltani - IEEE Transactions on Power Systems, 2024 - ieeexplore.ieee.org
Precise and timely fault diagnosis is a prerequisite for a distribution system to ensure
minimum downtime and maintain reliable operation. This necessitates access to a …
minimum downtime and maintain reliable operation. This necessitates access to a …
[HTML][HTML] Aperiodic small signal stability method for detection and mitigation of cascading failures in smart grids
The occurrence of cascading failures poses significant risks to the stability and reliability of
modern smart grids. This article presents a novel hybrid algorithm designed to assess and …
modern smart grids. This article presents a novel hybrid algorithm designed to assess and …
Graph Autoencoder-Based Power Attacks Detection for Resilient Electrified Transportation Systems
The interdependence of power and electrified transportation systems introduces new
challenges to the reliability and resilience of charging infrastructure. With the increasing …
challenges to the reliability and resilience of charging infrastructure. With the increasing …
Fault classification and localization in microgrids: Leveraging discrete wavelet transform and multi-machine learning techniques considering single point …
Currently, microgrids are becoming more prevalent. Therefore, it is crucial to develop robust
and reliable microgrid protection schemes. Researchers have recently explored various …
and reliable microgrid protection schemes. Researchers have recently explored various …
Spatio-temporal graph attention network-based detection of FDIA from smart meter data at geographically hierarchical levels
The power consumption data from residential households collected by smart meters exhibit
a diverse pattern temporally and among themselves. It is challenging to distinguish between …
a diverse pattern temporally and among themselves. It is challenging to distinguish between …
A Hybrid Real-Time Framework for Efficient Fussell-Vesely Importance Evaluation Using Virtual Fault Trees and Graph Neural Networks
The Fussell-Vesely Importance (FV) reflects the potential impact of a basic event on system
failure, and is crucial for ensuring system reliability. However, traditional methods for …
failure, and is crucial for ensuring system reliability. However, traditional methods for …