[HTML][HTML] A systematic review of machine learning applications in the operation of smart distribution systems

T Matijašević, T Antić, T Capuder - Energy reports, 2022 - Elsevier
Due to climate changes happening in the past few years, the necessity for the integration of
renewable energy sources and other low-carbon technologies is ever-growing. With the …

Consensus multi-agent reinforcement learning for volt-var control in power distribution networks

Y Gao, W Wang, N Yu - IEEE Transactions on Smart Grid, 2021 - ieeexplore.ieee.org
Volt-VAR control (VVC) is a critical application in active distribution network management
system to reduce network losses and improve voltage profile. To remove dependency on …

Model-augmented safe reinforcement learning for Volt-VAR control in power distribution networks

Y Gao, N Yu - Applied Energy, 2022 - Elsevier
Volt-VAR control (VVC) is a critical tool to manage voltage profiles and reactive power flow
in power distribution networks by setting voltage regulating and reactive power …

Deep reinforcement learning-based adaptive voltage control of active distribution networks with multi-terminal soft open point

P Li, M Wei, H Ji, W **, H Yu, J Wu, H Yao… - International Journal of …, 2022 - Elsevier
The integration of highly penetrated distributed generators (DGs) aggravates the rise of
voltage violations in distribution networks. Connected by multi-terminal soft open points (M …

Load flow-based method for nontechnical electrical loss detection and location in distribution systems using smart meters

TSD Ferreira, FCL Trindade… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
This work presents an innovative load flow-based approach that uses measurement data
from smart meters to detect and locate nontechnical losses (NTL). This approach is focused …

Affinely adjustable robust volt/var control without centralized computations

FU Nazir, BC Pal, RA Jabr - IEEE Transactions on Power …, 2022 - ieeexplore.ieee.org
This paper proposes a completely non-centralized Volt/VAr control (VVC) algorithm for
active distribution networks which are faced with voltage magnitude violations due to the …

Reactive power optimization of distribution network based on graph convolutional network

L Wenlong, YU Yun, W Yusen… - Power System …, 2020 - epjournal.csee.org.cn
The construction of advanced metering infrastructure and the rapid development of deep
learning technology make it possible to quickly find the optimal strategy for reactive power …

The Internet of Things-Enabled Smart City: An In-Depth Review of Its Domains and Applications

A Meydani, A Ramezani… - 2023 13th International …, 2023 - ieeexplore.ieee.org
The Internet of Things (IoT) refers to a comprehensive system that integrates diverse devices
and technologies, thereby eliminating the need for human involvement. IoT has played a …

A Deep Reinforcement Learning Design for Virtual Synchronous Generators Accommodating Modular Multilevel Converters

M Yang, X Wu, MC Loveth - Applied Sciences, 2023 - mdpi.com
The deep reinforcement learning (DRL) technique has gained attention for its potential in
designing “virtual network” controllers. This skill utilizes a novel solution that can avoid the …

A support vector regression based model predictive control for volt-var optimization of distribution systems

E Pourjafari, M Reformat - IEEE Access, 2019 - ieeexplore.ieee.org
This paper proposes a support vector regression (SVR)-based model predictive control
(MPC) for the volt-var optimization (VVO) of electrical distribution systems. First …