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[HTML][HTML] A systematic review of machine learning applications in the operation of smart distribution systems
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 …
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
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 …
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
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 …
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
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 …
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 …
from smart meters to detect and locate nontechnical losses (NTL). This approach is focused …
Affinely adjustable robust volt/var control without centralized computations
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 …
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 …
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
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 …
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 …
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 …
(MPC) for the volt-var optimization (VVO) of electrical distribution systems. First …