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Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning [article]

Sayak Mukherjee, Renke Huang, Qiuhua Huang, Thanh Long Vu, Tianzhixi Yin
2021 arXiv   pre-print
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]

Renke Huang, Yujiao Chen, Tianzhixi Yin, Xinya Li, Ang Li, Jie Tan, Wenhao Yu, Yuan Liu, Qiuhua Huang
2020 arXiv   pre-print
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

Di Cao, Weihao Hu, Junbo Zhao, Guozhou Zhang, Bin Zhang, Zhou Liu, Zhe Chen, Frede Blaabjerg
2020 Journal of Modern Power Systems and Clean Energy  
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]

Xin Chen, Guannan Qu, Yujie Tang, Steven Low, Na Li
2021 arXiv   pre-print
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  
., +, 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]

Brandon L. Thayer, Thomas J. Overbye
2020 arXiv   pre-print
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]

David Biagioni, Xiangyu Zhang, Dylan Wald, Deepthi Vaidhynathan, Rohit Chintala, Jennifer King, Ahmed S. Zamzam
2021 arXiv   pre-print
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

Shitu Zhang, Zhixun Zhu, Yang Li
2021 Energies  
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]

Shitu Zhang, Zhixun Zhu, Yang Li
2021 arXiv   pre-print
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

Daekyum Kim, Sang-Hun Kim, Taekyoung Kim, Brian Byunghyun Kang, Minhyuk Lee, Wookeun Park, Subyeong Ku, DongWook Kim, Junghan Kwon, Hochang Lee, Joonbum Bae, Yong-Lae Park (+2 others)
2021 PLoS ONE  
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  
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]

Aviral Chharia, Nishi Mehta, Shivam Gupta, Shivam Prajapati
2021 arXiv   pre-print
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

Nastaran Gholizadeh, Petr Musilek
2021 Energies  
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

Jun Guo, Sunwoo Kim, Henk Wymeersch, Walid Saad, Wei Chen
2019 IEEE Transactions on Vehicular Technology  
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

Tianhan Gao, Wei Lu
2020 iScience  
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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