Applications of physics-informed neural networks in power systems-a review
The advances of deep learning (DL) techniques bring new opportunities to numerous
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals
Vibration measurement and monitoring are essential in a wide variety of applications.
Vibration measurements are critical for diagnosing industrial machinery malfunctions …
Vibration measurements are critical for diagnosing industrial machinery malfunctions …
The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study
Deep learning (DL)-based methods have advanced the field of Prognostics and Health
Management (PHM) in recent years, because of their powerful feature representation ability …
Management (PHM) in recent years, because of their powerful feature representation ability …
Graph neural networks for anomaly detection in industrial Internet of Things
The Industrial Internet of Things (IIoT) plays an important role in digital transformation of
traditional industries toward Industry 4.0. By connecting sensors, instruments, and other …
traditional industries toward Industry 4.0. By connecting sensors, instruments, and other …
A review of graph neural networks and their applications in power systems
W Liao, B Bak-Jensen, JR Pillai… - Journal of Modern …, 2021 - ieeexplore.ieee.org
Deep neural networks have revolutionized many machine learning tasks in power systems,
ranging from pattern recognition to signal processing. The data in these tasks are typically …
ranging from pattern recognition to signal processing. The data in these tasks are typically …
Graph convolutional network-based method for fault diagnosis using a hybrid of measurement and prior knowledge
Deep-neural network-based fault diagnosis methods have been widely used according to
the state of the art. However, a few of them consider the prior knowledge of the system of …
the state of the art. However, a few of them consider the prior knowledge of the system of …
Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions
Smart grid is the new trend for clean, sustainable, efficient and reliable energy generation,
delivery and use. To ensure stable and secure operation is essential for the smart grid …
delivery and use. To ensure stable and secure operation is essential for the smart grid …
Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review
Meteorological changes urge engineering communities to look for sustainable and clean
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
Machine learning applications in power system fault diagnosis: Research advancements and perspectives
Newer generation sources and loads are posing new challenges to the conventional power
system protection schemes. Adaptive and intelligent protection methodology, based on …
system protection schemes. Adaptive and intelligent protection methodology, based on …
A review of fault location and classification methods in distribution grids
The evolution of the conventional power systems to smart grids has changed the way to
conceive and operate them. The part of the grid evolving the most is the distribution grid …
conceive and operate them. The part of the grid evolving the most is the distribution grid …