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Adaptive Power System Emergency Control using Deep Reinforcement Learning
[article]
2019
arXiv
pre-print
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To address these challenges, for the first time, this paper developed novel adaptive emergency control schemes using deep reinforcement learning (DRL), by leveraging the high-dimensional feature extraction ...
Power system emergency control is generally regarded as the last safety net for grid security and resiliency. ...
Guanji Hou for his valuable suggestions and assistance in developing the MPC-based emergency control method in this paper. ...
arXiv:1903.03712v2
fatcat:tlnl7if7uvb25ir3oijhihysti
Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems
[article]
2021
arXiv
pre-print
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Recently, deep reinforcement learning (RL) has been regarded and adopted as a promising approach that can significantly reduce the amount of load shedding. ...
However, like most existing machine learning (ML)-based control techniques, RL control usually cannot guarantee the safety of the systems under control. ...
CONCLUSIONS In this paper, we presented a highly scalable and safe deep reinforcement learning algorithm for power system voltage stability control using load shedding. ...
arXiv:2103.14186v2
fatcat:6l6x566fjjaxxeiqxmjdey4wii
Stability of intelligent energy system and intelligent control methods
2019
E3S Web of Conferences
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In modern power systems, a variety of both objects and the tools of control is expected to be much larger than before. ...
Therefore, the coordination of control of both normal and emergency modes of the systems takes on a special role and should become more intelligent. ...
Restoration of the power system [21] Abbreviations: RL -Reinforcement Learning; SL -Supervised Learning; DL -Deep Learning; DRL -Deep Reinforcement Learning; USL -Unsupervised Learning. ...
doi:10.1051/e3sconf/201913901051
fatcat:gka6ybgyhfexnhkhh6gmu4anji
Safe Reinforcement Learning for Emergency LoadShedding of Power Systems
[article]
2020
arXiv
pre-print
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Recently, deep reinforcement learning(RL) has been regarded and adopted as a promising approach leveraging massive data for fast and adaptive grid control. ...
control methods for power systems. ...
CONCLUSIONS In this paper, we presented a highly scalable and safe deep reinforcement learning algorithm for power system voltage stability control using load shedding. ...
arXiv:2011.09664v1
fatcat:pj3wk5i22velxgymlpnrjr6osu
Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning
[article]
2022
arXiv
pre-print
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Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. ...
In this paper, we mitigate these limitations by developing a novel deep meta reinforcement learning (DMRL) algorithm. ...
In this paper, we tackle this problem by developing a novel deep meta-reinforcement learning (DMRL) algorithm and applying it to learn and adapt power system emergency control policies against voltage ...
arXiv:2101.05317v2
fatcat:55lmzubo5fhrfjsderg3csbfae
Adaptive Traffic Control System using Reinforcement Learning
2020
International Journal of Engineering Research and
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In this system, the Reinforcement Learning algorithm was used to determine optimal traffic light configuration and using deep Neural Networks the obtained results were used to extract the features required ...
To handle such traffic either expansion of road networks or adaptive traffic control system which handles such traffic intelligently. ...
Adaptive Traffic
Signal Control:
Deep
reinforcement
learning algorithm
with experience
replay and target
network
Reinforcement learning
algorithm using
experience replay and
target network ...
doi:10.17577/ijertv9is020159
fatcat:wk6t2z7rvfdktmiogdkdvs2k4a
(Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives
2019
Annual Reviews in Control
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This paper reviews existing works on (deep) reinforcement learning considerations in electric power system control. ...
The works are reviewed as they relate to electric power system operating states (normal, preventive, emergency, restorative) and control levels (local, household, microgrid, subsystem, wide-area). ...
This paper reviews considerations of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) to design advanced controls in electric power systems. ...
doi:10.1016/j.arcontrol.2019.09.008
fatcat:2sldrcrykzd7bectmvrn3nisky
A Real-Time Intelligent Energy Management Strategy for Hybrid Electric Vehicles Using Reinforcement Learning
2021
IEEE Access
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for evaluating the control performance using the reinforcement learning algorithm. ...
Further, the performances of the intelligent controller using reinforcement learning are compared to those of the adaptive ECMS used in IEEE VTS Challenge 2018, as shown in Table V and Fig. 18 . ...
doi:10.1109/access.2021.3079903
fatcat:roxkwt5jpvflpgytft7zubf3va
Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives
2017
IFAC-PapersOnLine
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In this paper, we review past (including very recent) research considerations in using reinforcement learning (RL) to solve electric power system decision and control problems. ...
The RL considerations are reviewed in terms of specific electric power system problems, type of control and RL method used. ...
We also provide a few research directions involving reinforcement learning. ...
doi:10.1016/j.ifacol.2017.08.1217
fatcat:sppb7cgnvjaz5hmv5nzx4z7sfy
Special Issue on Artificial-Intelligence-Powered Edge Computing for Internet of Things
2020
IEEE Internet of Things Journal
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An effective online learning algorithm using adaptive window pattern clustering (AWPC) is proposed to update prediction models online. ...
access-based MEC system for high-quality immersive VR video services by jointly optimizing the viewport rendering offloading and downlink transmit power control. ...
doi:10.1109/jiot.2020.3019948
fatcat:mogalqnhnnaqpbxb7zivzdhvry
Special Issue on Deep Reinforcement Learning for Emerging IoT Systems
2020
IEEE Internet of Things Journal
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Guest Editorial Special Issue on Deep Reinforcement Learning for Emerging IoT Systems N OWADAYS we are witnessing the formation of a massive Internet-of-Things (IoT) ecosystem that integrates a variety ...
The deep Q-learning (DQN) algorithm is used to generate the watermarked position adaptively, making the watermarked positions secure yet close to the original design. ...
doi:10.1109/jiot.2020.2998256
fatcat:rct75tsesbh7lkjmhuyogfi4ym
Table of Contents
2021
2021 IEEE 46th Conference on Local Computer Networks (LCN)
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Progressive Mesh-Based HTTP Adaptive Augmented Reality Streaming System
257
Anchor-Oriented Time and Phase-Based Concurrent Self-Localization Using UWB Radios
265
A Deep Reinforcement Learning ...
Learning Approach
233
A Formal Method for Evaluating the Performance of TSN Traffic Shapers Using UPPAAL
241
Transfer Learning-Based Accelerated Deep Reinforcement Learning for 5G RAN Slicing
249 ...
doi:10.1109/lcn52139.2021.9524933
fatcat:bopsc4l2qrc7bobzfyb6343iou
Power flow adjustment for smart microgrid based on edge computing and multi-agent deep reinforcement learning
2021
Journal of Cloud Computing: Advances, Systems and Applications
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To address this challenge, we consider a power control framework combining edge computing and reinforcement learning, which makes full use of edge nodes to sense network state and control power equipment ...
Additionally, we focus on the non-convergence problem of power flow calculation, and combine deep reinforcement learning and multi-agent methods to realize intelligent decisions, with designing the model ...
Huang proposes an adaptive emergency control scheme based on the feature extraction and nonlinear generalization capabilities of deep reinforcement learning for complex power systems [28] . ...
doi:10.1186/s13677-021-00259-1
fatcat:o6b4fufcqzhrhe3kawmpfsftbu
Reinforcement Learning in Dynamic Task Scheduling: A Review
2020
SN Computer Science
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Reinforcement Learning is an emergent technology which has been able to solve the problem of the optimal task and resource scheduling dynamically. ...
This review paper is about a research study that focused on Reinforcement Learning techniques that have been used for dynamic task scheduling. ...
SCOOT, SCATS, OPAC and RHODES are some of the adaptive signal control systems available [16] . ...
doi:10.1007/s42979-020-00326-5
fatcat:egp6vgpetbcwdasm45vunmo3n4
Energy-Efficient Sensor Calibration Based on Deep Reinforcement Learning
2019
International Journal of Artificial Intelligence and Applications for Smart Devices
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Our proposed model using Deep Q learning (DQN) enables IoT sensors to maximize its resource utilization. ...
Reinforcement learning (RL) has been received much attention from researchers and now widely applied in many study fields to achieve intelligent automation. ...
Deep reinforcement learning (deep RL) has emerged as an effective solution for learning how to mimic decision flows of very complex real models [4] . ...
doi:10.21742/ijaiasd.2019.7.1.02
fatcat:rspce7htu5hbliek3g7kvljmbu
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