Deep learning in smart grid technology: A review of recent advancements and future prospects

M Massaoudi, H Abu-Rub, SS Refaat, I Chihi… - IEEE …, 2021 - ieeexplore.ieee.org
The current electric power system witnesses a significant transition into Smart Grids (SG) as
a promising landscape for high grid reliability and efficient energy management. This …

Machine learning driven smart electric power systems: Current trends and new perspectives

MS Ibrahim, W Dong, Q Yang - Applied Energy, 2020 - Elsevier
The current power systems are undergoing a rapid transition towards their more active,
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …

Review on the research and practice of deep learning and reinforcement learning in smart grids

D Zhang, X Han, C Deng - CSEE Journal of Power and Energy …, 2018 - ieeexplore.ieee.org
Smart grids are the developmental trend of power systems and they have attracted much
attention all over the world. Due to their complexities, and the uncertainty of the smart grid …

Deep learning methods and applications for electrical power systems: A comprehensive review

AK Ozcanli, F Yaprakdal… - International Journal of …, 2020 - Wiley Online Library
Over the past decades, electric power systems (EPSs) have undergone an evolution from an
ordinary bulk structure to intelligent flexible systems by way of advanced electronics and …

Deep learning in electrical utility industry: A comprehensive review of a decade of research

M Mishra, J Nayak, B Naik, A Abraham - Engineering Applications of …, 2020 - Elsevier
Smart-grid (SG) is a new revolution in the electrical utility industry (EUI) over the past
decade. With each moving day, some new advanced technologies are coming into the …

A Survey on Deep Learning Role in Distribution Automation System: A New Collaborative Learning-to-Learning (L2L) Concept

M Jafari, A Kavousi-Fard, M Dabbaghjamanesh… - IEEE …, 2022 - ieeexplore.ieee.org
This paper focuses on a powerful and comprehensive overview of Deep Learning (DL)
techniques on Distribution Automation System (DAS) applications to provide a complete …

Deep learning in power systems research: A review

M Khodayar, G Liu, J Wang… - CSEE Journal of Power …, 2020 - ieeexplore.ieee.org
With the rapid growth of power systems measurements in terms of size and complexity,
discovering statistical patterns for a large variety of real-world applications such as …

Transformation of smart grid using machine learning

S Azad, F Sabrina, S Wasimi - 2019 29th Australasian …, 2019 - ieeexplore.ieee.org
With the advent of distributed and renewable energy sources, maintaining the stability of
power grid is becoming increasingly difficult. Traditional power grid can be transformed into …

A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids

S Aslam, H Herodotou, SM Mohsin, N Javaid… - … and Sustainable Energy …, 2021 - Elsevier
Microgrids have recently emerged as a building block for smart grids combining distributed
renewable energy sources (RESs), energy storage devices, and load management …

Towards intelligent power electronics-dominated grid via machine learning techniques

OH Abu-Rub, AY Fard, MF Umar… - IEEE Power …, 2021 - ieeexplore.ieee.org
Nowadays, to meet the vision of employing 100% renewable-based electricity generation,
the conventional power system is evolving into power electronics-dominated grid (PEDG) …