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Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning
[article]
2021
arXiv
pre-print
Preserved Fulltext
This paper presents a novel hierarchical deep reinforcement learning (DRL) based design for the voltage control of power grids. ...
Existing voltage control techniques suffer from the issues of speed of operation, optimal coordination between different locations, and scalability. ...
Index Terms-Emergency voltage control, deep reinforcement learning, hierarchical learning control, scalable learning.
I. ...
arXiv:2102.00077v1
fatcat:lcme6ztwpne5vbn2za2hmremii
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2006.12667v1 fatcat:w2av3m346vhmhpiyyt6uunkbcy
Deep reinforcement learning (DRL) was regarded and adopted as a promising approach for fast and adaptive grid stability control in recent years. ...
Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability. ...
This paper focuses on developing an accelerated deep reinforcement learning (DRL)-based control method to make load shedding for emergency voltage control fast, adaptive, and scalable. ...
arXiv:2006.12667v2
fatcat:vhhl5ipzwvbfthk4regiolu63y
Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review
2020
Journal of Modern Power Systems and Clean Energy
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Index Terms--Reinforcement learning, deep reinforcement learning, power system operation and control, optimization. ...
Among them, reinforcement learning (RL) is one of the most widely promoted methods for control and optimization problems. ...
Owing to the complexity of hierarch structure and the lack of a general hierarchical framework, applications of RL for hierarchical control are rare in power and energy systems. ...
doi:10.35833/mpce.2020.000552
fatcat:42bllvvymfhfxbh42t6a46q4tq
Reinforcement Learning for Decision-Making and Control in Power Systems: Tutorial, Review, and Vision
[article]
2021
arXiv
pre-print
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As a result, data-driven control techniques, especially reinforcement learning (RL), have attracted surging attention in recent years. ...
We illustrate RL-based models and solutions in three key applications, frequency regulation, voltage control, and energy management. ...
For example, reference [83] develops a scalable LSPI-based voltage control scheme, which uses the trick that sequentially learns a separate approximate Qfunctions for each component of the action, whilst ...
arXiv:2102.01168v4
fatcat:ibjelwrjffg5bm7eg3dlw6u3ne
2020 Index IEEE Transactions on Power Systems Vol. 35
2020
IEEE Transactions on Power Systems
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., +, TPWRS March 2020
1109-1119
A Data-Driven Multi-Agent Autonomous Voltage Control Framework Using
Deep Reinforcement Learning. ...
Kwon,
J., +, TPWRS Jan. 2020 773-781
Games
A Data-Driven Multi-Agent Autonomous Voltage Control Framework Using
Deep Reinforcement Learning. ...
doi:10.1109/tpwrs.2020.3040894
fatcat:jjw2rnzr2re6fejvariekzr5uy
Deep Reinforcement Learning for Electric Transmission Voltage Control
[article]
2020
arXiv
pre-print
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A subset of machine learning known as deep reinforcement learning (DRL) has recently shown promise in performing tasks typically performed by humans. ...
This paper applies DRL to the transmission voltage control problem, presents open-source DRL environments for voltage control, proposes a novel modification to the "deep Q network" (DQN) algorithm, and ...
Deep Reinforcement Learning for Electric Transmission Voltage Control Brandon L. Thayer, Member, IEEE, Thomas J. Overbye, Fellow, IEEE, ©2020 IEEE. Personal use of this material is permitted. ...
arXiv:2006.06728v2
fatcat:fjqvtfqwwjgpri3mrfceat32g4
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems
[article]
2021
arXiv
pre-print
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To highlight PowerGridworld's key features, we present two case studies and demonstrate learning MARL policies using both OpenAI's multi-agent deep deterministic policy gradient (MADDPG) and RLLib's proximal ...
learning (RL). ...
Reinforcement learning (RL) approaches have shown great potential in several power systems control and load management tasks [1] - [4] . ...
arXiv:2111.05969v1
fatcat:jisj34ccbnaghldi2oiobllfk4
A Critical Review of Data-Driven Transient Stability Assessment of Power Systems: Principles, Prospects and Challenges
2021
Energies
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Since traditional time-domain simulations and direct method cannot meet the actual operation requirements of power systems, data-driven TSA has attracted growing attention from both academia and industry ...
This paper makes a comprehensive review from the following four aspects: feature extraction and selection, model construction, online learning and rule extraction; and then, summarizes the challenges and ...
Therefore, how to use deep learning technology to analyze and reason about graph structure data has attracted widespread attention from scholars. ...
doi:10.3390/en14217238
fatcat:ioui7lgvlvb6ne33peldzmmdla
A critical review of data-driven transient stability assessment of power systems: principles, prospects and challenges
[article]
2021
arXiv
pre-print
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Since traditional time-domain simulations and direct method cannot meet the actual operation requirements of power systems, data-driven TSA has attracted growing attention from both academia and industry ...
This paper makes a comprehensive review from the following four aspects: feature extraction and selection, model construction, online learning and rule extraction; and then, summarizes the challenges and ...
Therefore, how to use deep learning technology to analyze and reason about graph structure data has attracted widespread attention from scholars. ...
arXiv:2111.00978v1
fatcat:byrrmsopbfdnxjghsw4vn7p4im
Review of machine learning methods in soft robotics
2021
PLoS ONE
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However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity ...
followed by a summary of the existing machine learning methods for soft robots. ...
To accomplish such tasks, some papers have used reinforcement learning algorithms to control the robots. ...
doi:10.1371/journal.pone.0246102
pmid:33600496
pmcid:PMC7891779
fatcat:alu4zm72irespj6wydikzjb6ie
2019 Index IEEE Transactions on Industrial Informatics Vol. 15
2019
IEEE Transactions on Industrial Informatics
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Li, A., +, TII Jan. 2019 366-376 Scalable Healthcare Assessment for Diabetic Patients Using Deep Learning on Multiple GPUs. ...
., +, TII Dec. 2019 6379-6388
Scalable Healthcare Assessment for Diabetic Patients Using Deep Learning
on Multiple GPUs. ...
doi:10.1109/tii.2020.2968165
fatcat:utk3ywxc6zgbdbfsys5f4otv7u
Recent Trends in Artificial Intelligence-inspired Electronic Thermal Management
[article]
2021
arXiv
pre-print
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The present study discusses in detail, the current uses of deep learning in the domain of 'electronic' thermal management. ...
The rise of computation-based methods in thermal management has gained immense attention in recent years due to the ability of deep learning to solve complex 'physics' problems, which are otherwise difficult ...
The 3D equivalent of CNNs is 3D CNNs used when the dataset comprises
3D structures/ matrices.
2.4 Other Deep Learning Architectures and Domains
Other Deep Learning architectures (Ian Goodfellow and ...
arXiv:2112.14837v1
fatcat:w5jdrf55mnfb3iqfnrld3mqz4i
Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges
2021
Energies
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In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. ...
Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. ...
Servicing agents used reinforced Q-learning to decide about the most proper voltage control action. ...
doi:10.3390/en14123654
fatcat:25fv4mw2dfan5c23lhi3l45tte
Guest Editorial: Introduction to the Special Section on Machine Learning-Based Internet of Vehicles: Theory, Methodology, and Applications
2019
IEEE Transactions on Vehicular Technology
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To promote the congestion detection, a robust hierarchical deep learning is proposed by Wang et al. for the task in the article "Robust Hierarchical Deep Learning for Vehicular Management." ...
algorithms and commonly used deep learning algorithms. ...
doi:10.1109/tvt.2019.2914747
fatcat:rrpckr7cczfdzmqy7nkbcnsdua
Machine learning toward advanced energy storage devices and systems
2020
iScience
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This paper reviews recent progresses in this emerging area, especially new concepts, approaches, and applications of machine learning technologies for commonly used energy storage devices (including batteries ...
Designing such systems involve a trade-off among a large set of parameters, whereas advanced control strategies need to rely on the instantaneous status of many indicators. ...
Reinforcement learning and deep reinforcement learning are increasingly being used for the management of grids and microgrids. ...
doi:10.1016/j.isci.2020.101936
pmid:33458608
pmcid:PMC7797524
fatcat:oemawa46rbevjihjnf7zdc4vmm
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