An overview of artificial intelligence applications for power electronics
This article gives an overview of the artificial intelligence (AI) applications for power
electronic systems. The three distinctive life-cycle phases, design, control, and maintenance …
electronic systems. The three distinctive life-cycle phases, design, control, and maintenance …
Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance
Most existing data-driven power system short-term voltage stability assessment (STVSA)
approaches presume class-balanced input data. However, in practical applications, the …
approaches presume class-balanced input data. However, in practical applications, the …
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 …
[HTML][HTML] A systematic review of machine learning techniques related to local energy communities
In recent years, digitalisation has rendered machine learning a key tool for improving
processes in several sectors, as in the case of electrical power systems. Machine learning …
processes in several sectors, as in the case of electrical power systems. Machine learning …
Machine learning for sustainable energy systems
In recent years, machine learning has proven to be a powerful tool for deriving insights from
data. In this review, we describe ways in which machine learning has been leveraged to …
data. In this review, we describe ways in which machine learning has been leveraged to …
Perspectives on future power system control centers for energy transition
Today's power systems are seeing a paradigm shift under the energy transition, sparkled by
the electrification of demand, digitalisation of systems, and an increasing share of …
the electrification of demand, digitalisation of systems, and an increasing share of …
An innovative optimal 4E solar-biomass waste polygeneration system for power, methanol, and freshwater production
Modern polygeneration systems have increased the flexibility of production in energy
systems. In this paper, a polygeneration system for producing power, freshwater, and …
systems. In this paper, a polygeneration system for producing power, freshwater, and …
Deepopf: A deep neural network approach for security-constrained dc optimal power flow
We develop DeepOPF as a Deep Neural Network (DNN) approach for solving security-
constrained direct current optimal power flow (SC-DCOPF) problems, which are critical for …
constrained direct current optimal power flow (SC-DCOPF) problems, which are critical for …
[HTML][HTML] Physics-informed neural networks for ac optimal power flow
R Nellikkath, S Chatzivasileiadis - Electric Power Systems Research, 2022 - Elsevier
This paper introduces, for the first time to our knowledge, physics-informed neural networks
to accurately estimate the AC-Optimal Power Flow (AC-OPF) result and delivers rigorous …
to accurately estimate the AC-Optimal Power Flow (AC-OPF) result and delivers rigorous …
Deep reinforcement learning for smart grid operations: algorithms, applications, and prospects
With the increasing penetration of renewable energy and flexible loads in smart grids, a
more complicated power system with high uncertainty is gradually formed, which brings …
more complicated power system with high uncertainty is gradually formed, which brings …