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Adaptive Power System Emergency Control using Deep Reinforcement Learning [article]

Qiuhua Huang, Renke Huang, Weituo Hao, Jie Tan, Rui Fan, Zhenyu Huang
2019 arXiv   pre-print
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]

Thanh Long Vu and Sayak Mukherjee and Renke Huang and Qiuhua Hung
2021 arXiv   pre-print
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

Nikolai Voropai, Dmitry Efimov, Victor Kurbatsky, Nikita Tomin, N. Voropai, S. Senderov, A. Michalevich, H. Guliev
2019 E3S Web of Conferences  
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]

Thanh Long Vu and Sayak Mukherjee and Tim Yin and Renke Huang and and Jie Tan and Qiuhua Huang
2020 arXiv   pre-print
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]

Renke Huang, Yujiao Chen, Tianzhixi Yin, Qiuhua Huang, Jie Tan, Wenhao Yu, Xinya Li, Ang Li, Yan Du
2022 arXiv   pre-print
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

Kranti Shingate, Komal Jagdale, Yohann Dias, Fr. c Rodrigues Institute of Technology
2020 International Journal of Engineering Research and  
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

Mevludin Glavic
2019 Annual Reviews in Control  
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

Woong Lee, Haeseong Jeoung, Dohyun Park, Tacksu Kim, Heeyun Lee, Namwook Kim
2021 IEEE Access  
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

Mevludin Glavic, Raphaël Fonteneau, Damien Ernst
2017 IFAC-PapersOnLine  
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

Lei Yang, Xu Chen, Samir M. Perlaza, Junshan Zhang
2020 IEEE Internet of Things Journal  
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

Jia Hu, Peng Liu, Hong Liu, Obinna Anya, Yan Zhang
2020 IEEE Internet of Things Journal  
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)  
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

Tianjiao Pu, Xinying Wang, Yifan Cao, Zhicheng Liu, Chao Qiu, Ji Qiao, Shuhua Zhang
2021 Journal of Cloud Computing: Advances, Systems and Applications  
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

Chathurangi Shyalika, Thushari Silva, Asoka Karunananda
2020 SN Computer Science  
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

Akm Ashiquzzaman, School of Electronics and Computer Engineering, Chonnam National University, Gwangju, South Korea
2019 International Journal of Artificial Intelligence and Applications for Smart Devices  
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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