(Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works

Untitled work

(1995) · Cited by 10,558 · see citing works

A Brief Survey of Deep Reinforcement Learning

Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · arXiv · doi:10.48550/arxiv.1708.05866 · Cited by 4,733 · see citing works

Deep Reinforcement Learning: A Brief Survey

Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2743240 · Cited by 4,733 · see citing works

Power system dynamics : stability and control

Machowski, J., Bialek, J. W., & Bumby, J. R. (2008) · Durham Research Online (Durham University) · Cited by 2,351 · see citing works

An Introduction to Deep Reinforcement Learning

François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000071 · Cited by 1,353 · see citing works

Untitled work

(2015) · Journal of Machine Learning Research · Cited by 1,119 · see citing works

Recent Advances in Hierarchical Reinforcement Learning

Barto, A. G., & Mahadevan, S. (2003) · Discrete Event Dynamic Systems · doi:10.1023/a:1022140919877 · Cited by 1,043 · see citing works

Final report on the August 14, 2003 blackout in the United States and Canada : causes and recommendations

Muir, A., & Lopatto, J. (2004) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 948 · see citing works

Reinforcement Learning and Dynamic Programming Using Function Approximators

Buşoniu, L., Babuška, R., Schutter, B. D., & Ernst, D. (2010) · CRC Press eBooks · doi:10.1201/9781439821091 · Cited by 919 · see citing works

Optimal and Autonomous Control Using Reinforcement Learning: A Survey

Kiumarsi, B., Vamvoudakis, K. G., Modares, H., & Lewis, F. L. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2773458 · Cited by 908 · see citing works

Review of Internet of Things (IoT) in Electric Power and Energy Systems

Bedi, G., Venayagamoorthy, G. K., Singh, R., Brooks, R. R., & Wang, K. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2802704 · Cited by 756 · see citing works

A systems and control perspective of CPS security

Dibaji, S. M., Pirani, M., Flamholz, D. B., Annaswamy, A. M., Johansson, K. H., & Chakrabortty, A. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.04.011 · Cited by 562 · see citing works

An introduction to deep reinforcement learning

François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018) · doi:10.1561/9781680835397 · Cited by 544 · see citing works

Robust adversarial reinforcement learning

Pinto, L., Davidson, J., Sukthankar, R., & Gupta, A. (2017) · arXiv · doi:10.48550/arxiv.1703.02702 · Cited by 507 · see citing works

Reinforcement learning for control: Performance, stability, and deep approximators

Buşoniu, L., de Bruin, T., Tolić, D., Kober, J., & Palunko, I. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.09.005 · Cited by 468 · see citing works

A Generalized Path Integral Control Approach to Reinforcement Learning

Theodorou, E. A., Buchli, J., & Schaal, S. (2010) · Cited by 439 · see citing works

Deep-Reinforcement-Learning-Based Autonomous Voltage Control for Power Grid Operations

Duan, J., Shi, D., Diao, R., et al. (2019) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2941134 · Cited by 436 · see citing works

Energy Storage Technologies for High-Power Applications

Farhadi, M., & Mohammed, O. A. (2015) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2015.2511096 · Cited by 427 · see citing works

Adaptive Power System Emergency Control Using Deep Reinforcement Learning

Huang, Q., Huang, R., Hao, W., Tan, J., Fan, R., & Huang, Z. H. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2933191 · Cited by 424 · see citing works

Data-Driven Load Frequency Control for Stochastic Power Systems: A Deep Reinforcement Learning Method With Continuous Action Search

Yan, Z., & Xu, Y. H. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2881359 · Cited by 398 · see citing works

Power System Dynamics: Stability and Control

Padiyar, К. R. (1999) · Medical Entomology and Zoology · Cited by 384 · see citing works

A Survey on Energy Internet: Architecture, Approach, and Emerging Technologies

Wang, K., Yu, J., Yu, Y. P., et al. (2017) · IEEE Systems Journal · doi:10.1109/jsyst.2016.2639820 · Cited by 379 · see citing works

Performance Optimization for Blockchain-Enabled Industrial Internet of Things (IIoT) Systems: A Deep Reinforcement Learning Approach

Liu, M., Yu, F. R., Teng, Y., Leung, V. C. M., & Song, M. N. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2897805 · Cited by 365 · see citing works

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

Lei, L., Tan, Y., Zheng, K., Liu, S., Zhang, K., & Shen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988367 · Cited by 307 · see citing works

A Generalized Path Integral Control Approach to Reinforcement Learning

TheodorouEvangelos, BuchliJonas, & SchaalStefan (2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953033 · Cited by 282 · see citing works

An Adaptive Network-Based Reinforcement Learning Method for MPPT Control of PMSG Wind Energy Conversion Systems

Wei, C., Zhang, Z., Qiao, W., et al. (2016) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2016.2514370 · Cited by 276 · see citing works

Data-Driven Economic NMPC Using Reinforcement Learning

Gros, S., & Zanon, M. (2019) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2019.2913768 · Cited by 271 · see citing works

Systems & Control for the future of humanity, research agenda: Current and future roles, impact and grand challenges

Lamnabhi‐Lagarrigue, F., Annaswamy, A. M., Engell, S., Isaksson, A. J., Khargonekar, P. P., Murray, R. M., Nijmeijer, H., Samad, T., Tilbury, D. M., & den Hof, P. M. J. V. (2017) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2017.04.001 · Cited by 269 · see citing works

Q-Learning-Based Vulnerability Analysis of Smart Grid Against Sequential Topology Attacks

Yan, J., He, H., Zhong, X., & Tang, Y. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2607701 · Cited by 264 · see citing works

Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach

Kurt, M. N., Ogundijo, O., Li, C., & Wang, X. (2018) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2878570 · Cited by 263 · see citing works

Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives

Glavic, M., Fonteneau, R., & Ernst, D. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.1217 · Cited by 241 · see citing works

Evaluation of Reinforcement Learning-Based False Data Injection Attack to Automatic Voltage Control

Chen, Y., Huang, S., Liu, F., Wang, Z., & Sun, X. (2018) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2790704 · Cited by 229 · see citing works

Reinforcement Learning Versus Model Predictive Control: A Comparison on a Power System Problem

Ernst, D., Glavić, M., Capitanescu, F., & Wehenkel, L. A. (2008) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2008.2007630 · Cited by 214 · see citing works

Power Systems Stability Control: Reinforcement Learning Framework

Ernst, D., Glavić, M., & Wehenkel, L. A. (2004) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2003.821457 · Cited by 214 · see citing works

A reinforcement learning approach to automatic generation control

Ahamed, T. P. I., Rao, P. S. N., & SASTRY, P. S. (2002) · Electric Power Systems Research · doi:10.1016/s0378-7796(02)00088-3 · Cited by 205 · see citing works

The strategic power infrastructure defense (SPID) system. A conceptual design

Liu, C., Jung, J., Heydt, G. T., et al. (2000) · IEEE Control Systems · doi:10.1109/37.856178 · Cited by 202 · see citing works

Reinforcement-Learning-Based Optimal Control of Hybrid Energy Storage Systems in Hybrid AC–DC Microgrids

Duan, J., Yi, Z., Shi, D., Lin, C., Lu, X. B., & Wang, Z. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2896618 · Cited by 188 · see citing works

Accelerating Reinforcement Learning through Implicit Imitation

Price, B., & Boutilier, C. E. (2003) · Journal of Artificial Intelligence Research · doi:10.1613/jair.898 · Cited by 179 · see citing works

Reinforcement-Learning-Based Intelligent Maximum Power Point Tracking Control for Wind Energy Conversion Systems

Wei, C., Zhang, Z., Qiao, W., & Qu, L. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2015.2420792 · Cited by 178 · see citing works

Convolutional Neural Networks for Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control

Claessens, B., Vrancx, P., & Ruelens, F. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2629450 · Cited by 157 · see citing works

IEEE PSRC Report on Global Industry Experiences With System Integrity Protection Schemes (SIPS)

Madani, V., Novosel, D., Horowitz, S. H., Adamiak, M. G., Amantegui, J., Karlsson, D., Imai, S., & Apostolov, A. (2010) · IEEE Transactions on Power Delivery · doi:10.1109/tpwrd.2010.2046917 · Cited by 154 · see citing works

Multiagent-Based Reinforcement Learning for Optimal Reactive Power Dispatch

Xu, Y., Zhang, W., Liu, W., et al. (2012) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2218596 · Cited by 151 · see citing works

Load–frequency control: a GA-based multi-agent reinforcement learning

Daneshfar, F., & Bevrani, H. (2009) · IET Generation Transmission & Distribution · doi:10.1049/iet-gtd.2009.0168 · Cited by 149 · see citing works

A survey of preference-based reinforcement learning methods

Wirth, C., Akrour, R., Neumann, G. M., & Fürnkranz, J. (2017) · Repository KITopen (Karlsruhe Institute of Technology) · doi:10.5445/ir/1000118270 · Cited by 146 · see citing works

Big Data Analysis-Based Security Situational Awareness for Smart Grid

Wu, J., Ota, K., Dong, M., Li, J., & Wang, H. (2018) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2016.2616146 · Cited by 144 · cited 1× · see citing works

Reinforced Deterministic and Probabilistic Load Forecasting via $Q$ -Learning Dynamic Model Selection

Feng, C., Sun, M., & Zhang, J. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2937338 · Cited by 139 · see citing works

Tutorial on Stochastic Optimization in Energy—Part I: Modeling and Policies

Powell, W. B., & Meisel, S. (2015) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2015.2424974 · Cited by 137 · see citing works

Reinforcement Learning for Reactive Power Control

Vlachogiannis, J. G., & Hatziargyriou, N. D. (2004) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2004.831259 · Cited by 136 · see citing works

Real-time computer control of power systems

Liacco, T. E. D. (1974) · Proceedings of the IEEE · doi:10.1109/proc.1974.9541 · Cited by 133 · see citing works

Adaptation in load shedding under vulnerable operating conditions

Jung, J., Liu, C., Tanimoto, S. L., et al. (2002) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2002.805023 · Cited by 120 · see citing works

Multi-Agent Correlated Equilibrium Q(λ) Learning for Coordinated Smart Generation Control of Interconnected Power Grids

Yu, T., Wang, H. Z., Zhou, B., et al. (2014) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2014.2357079 · Cited by 118 · see citing works

Transient Stability Enhancement of Power Grid With Integrated Wide Area Control of Wind Farms and Synchronous Generators

Yousefian, R., Bhattarai, R., & Kamalasadan, S. (2017) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2017.2676138 · Cited by 117 · see citing works

A Novel Equivalent Model of Active Distribution Networks Based on LSTM

Zheng, C., Wang, S., Liu, Y., Liu, C., Xie, W. C., Fang, C., & Liu, S. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2885219 · Cited by 108 · see citing works

Safe Exploration Algorithms for Reinforcement Learning Controllers

Mannucci, T., Kampen, E.-J. V., de Visser, C. C., & Chu, Q. P. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2654539 · Cited by 104 · see citing works

Stochastic Optimal Relaxed Automatic Generation Control in Non-Markov Environment Based on Multi-Step $Q(\lambda)$ Learning

Yu, T., Zhou, B., Chan, K. W., et al. (2011) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2010.2102372 · Cited by 103 · see citing works

Fault-Tolerant Controller Design for a Class of Nonlinear MIMO Discrete-Time Systems via Online Reinforcement Learning Algorithm

Wang, Z., Liu, L., Zhang, H., & Xiao, G. (2015) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2015.2478885 · Cited by 102 · see citing works

Controls for Smart Grids: Architectures and Applications

Samad, T., & Annaswamy, A. M. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2707326 · Cited by 100 · see citing works

Defending Against Data Integrity Attacks in Smart Grid: A Deep Reinforcement Learning-Based Approach

An, D., Yang, Q., Liu, W., & Zhang, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2933020 · Cited by 99 · see citing works

A Multiagent Design for Power Distribution Systems Automation

Ghorbani, M. J., Choudhry, M. A., & Feliachi, A. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2453884 · Cited by 96 · see citing works

A survey of preference-based reinforcement learning methods

WirthChristian, AkrourRiad, NeumannGerhard, & FürnkranzJohannes (2017) · Journal of Machine Learning Research · doi:10.5555/3122009.3208017 · Cited by 94 · see citing works

A Hybrid Multiagent Framework With Q-Learning for Power Grid Systems Restoration

Ye, D., Zhang, M., & Sutanto, D. (2011) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2011.2157180 · Cited by 93 · see citing works

Control of a Point Absorber Using Reinforcement Learning

Anderlini, E., Forehand, D. I. M., Stansell, P., et al. (2016) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2016.2568754 · Cited by 91 · see citing works

Lifelong Learning for Complementary Generation Control of Interconnected Power Grids With High-Penetration Renewables and EVs

Zhang, X., Yu, T., Pan, Z. N., et al. (2017) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2017.2767318 · Cited by 91 · see citing works

Memetic reinforcement learning based maximum power point tracking design for PV systems under partial shading condition

Zhang, X., Li, S., He, T., Yang, B. F., Yu, T., Li, H., Jiang, L., & Sun, L. (2019) · Energy · doi:10.1016/j.energy.2019.03.053 · Cited by 81 · see citing works

Reinforcement Learning Based Real-Time Wide-Area Stabilizing Control Agents to Enhance Power System Stability

Hadidi, R., & Jeyasurya, B. (2013) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2012.2235864 · Cited by 79 · see citing works

Artificial emotional reinforcement learning for automatic generation control of large‐scale interconnected power grids

Yin, L., Yu, T., Zhou, L., et al. (2017) · IET Generation Transmission & Distribution · doi:10.1049/iet-gtd.2016.1734 · Cited by 79 · see citing works

Enhancing Power Quality in Microgrids With a New Online Control Strategy for DSTATCOM Using Reinforcement Learning Algorithm

Bagheri, M., Nurmanova, V., Abedinia, O., et al. (2018) · IEEE Access · doi:10.1109/access.2018.2852941 · Cited by 73 · see citing works

Q-Learning-Based Damping Control of Wide-Area Power Systems Under Cyber Uncertainties

Duan, J., Xu, H., & Liu, W. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2711599 · Cited by 72 · see citing works

Robust reinforcement learning control with static and dynamic stability

Kretchmar, R. M., Young, P. M., Anderson, C. W., et al. (2001) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.670 · Cited by 70 · see citing works

R( λ ) imitation learning for automatic generation control of interconnected power grids

Yu, T., Zhou, B., Chan, K. W., et al. (2012) · Automatica · doi:10.1016/j.automatica.2012.05.043 · Cited by 56 · see citing works

Experience Selection in Deep Reinforcement Learning for Control

de Bruin, T., Kober, J., Tuyls, K., & Babuška, R. (2018) · Research Repository (Delft University of Technology) · doi:10.5555/3291125.3291134 · Cited by 56 · see citing works

Sequential topology recovery of complex power systems based on reinforcement learning

Wu, J., Fang, B., Fang, J., Chen, X., & Tse, C. K. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.122487 · Cited by 55 · see citing works

Online tuning of a supervisory fuzzy controller for low-energy building system using reinforcement learning

Yu, Z., & Dexter, A. L. (2010) · Control Engineering Practice · doi:10.1016/j.conengprac.2010.01.018 · Cited by 52 · see citing works

An Adaptive Variable Leaky Least Mean Square Control Scheme for Grid Integration of a PV System

Bag, A., Subudhi, B., & Ray, P. K. (2019) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2019.2929551 · Cited by 50 · see citing works

Approximate Value Iteration in the Reinforcement Learning Context. Application to Electrical Power System Control.

Ernst, D., Glavić, M., Geurts, P., et al. (2005) · International Journal of Emerging Electric Power Systems · doi:10.2202/1553-779x.1066 · Cited by 41 · see citing works

Control of a Realistic Wave Energy Converter Model Using Least-Squares Policy Iteration

Anderlini, E., Forehand, D. I. M., Bannon, E., et al. (2017) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2017.2696060 · Cited by 40 · see citing works

Distributed Undervoltage Load Shedding

Otomega, B., Glavić, M., & Cutsem, T. V. (2007) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2007.907589 · Cited by 38 · see citing works

Equivalent modeling of active distribution network considering the spatial uncertainty of renewable energy resources

Shang, X., Li, Z., Zheng, J., et al. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2019.04.029 · Cited by 38 · see citing works

Robots with instincts

Adami, C. (2015) · Nature · doi:10.1038/521426a · Cited by 37 · see citing works

IEEE PES General Meeting

(2019) · IEEE Electrification Magazine · doi:10.1109/mele.2019.2951911 · Cited by 36 · see citing works

An integrated critic-actor neural network for reinforcement learning with application of DERs control in grid frequency regulation

Sun, J., Zhu, Z., Li, H., et al. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2019.04.011 · Cited by 36 · see citing works

Design and Real-Time Implementation of Optimal Power System Wide-Area System-Centric Controller Based on Temporal Difference Learning

Yousefian, R., & Kamalasadan, S. (2015) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2015.2466622 · Cited by 33 · see citing works

Reinforcement learning approach for congestion management and cascading failure prevention with experimental application

Zarrabian, S., Belkacemi, R., & Babalola, A. A. (2016) · Electric Power Systems Research · doi:10.1016/j.epsr.2016.06.041 · Cited by 33 · see citing works

Advancing systems and control research in the era of ML and AI

Khargonekar, P. P., & Dahleh, M. A. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.04.001 · Cited by 33 · see citing works

Power System Security Under False Data Injection Attacks With Exploitation and Exploration Based on Reinforcement Learning

Wang, Z., Chen, Y., Liu, F., et al. (2018) · IEEE Access · doi:10.1109/access.2018.2856520 · Cited by 33 · see citing works

Model‐free adaptive learning control scheme for wind turbines with doubly fed induction generators

Abouheaf, M., Gueaieb, W., & Sharaf, A. M. (2018) · IET Renewable Power Generation · doi:10.1049/iet-rpg.2018.5353 · Cited by 32 · see citing works

Cognitive Risk Control for Mitigating Cyber-Attack in Smart Grid

Oozeer, M. I., & Haykin, S. (2019) · IEEE Access · doi:10.1109/access.2019.2939089 · Cited by 31 · see citing works

Design of a resistive brake controller for power system stability enhancement using reinforcement learning

Glavić, M. (2005) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2005.847339 · Cited by 30 · see citing works

Hierarchical correlated Q-learning for multi-layer optimal generation command dispatch

Yu, T., Zhang, X., Zhou, B., & Chan, K. W. (2015) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2015.11.057 · Cited by 29 · see citing works

Multiobjective Reinforcement Learning-Based Intelligent Approach for Optimization of Activation Rules in Automatic Generation Control

Wang, H., Lei, Z., Zhang, X., Peng, J., & Jiang, H. Q. (2019) · IEEE Access · doi:10.1109/access.2019.2894756 · Cited by 27 · see citing works

A Neural Network Based Automatic Generation Controller Design through Reinforcement Learning

Ahamed, T. P. I., S, N. R. P., & Sastry, P. S. (2006) · International Journal of Emerging Electric Power Systems · doi:10.2202/1553-779x.1141 · Cited by 26 · see citing works

Energy Function Inspired Value Priority Based Global Wide-Area Control of Power Grid

Yousefian, R., & Kamalasadan, S. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2555909 · Cited by 26 · see citing works

Load frequency regulation for multi‐area power system using integral reinforcement learning

Abouheaf, M., Gueaieb, W., & Sharaf, A. M. (2019) · IET Generation Transmission & Distribution · doi:10.1049/iet-gtd.2019.0218 · Cited by 26 · see citing works

Transient stability emergency control combining open-loop and closed-loop techniques

Ruiz‐Vega, D., Glavić, M., & Ernst, D. (2004) · IEEE Power Engineering Society General Meeting · doi:10.1109/pes.2003.1270929 · Cited by 23 · see citing works

Reinforcement learning tuned decentralized synergetic control of power systems

Ademoye, T., & Feliachi, A. (2012) · Electric Power Systems Research · doi:10.1016/j.epsr.2011.11.024 · Cited by 22 · see citing works

Comparing Policy Gradient and Value Function Based Reinforcement Learning Methods in Simulated Electrical Power Trade

Lincoln, R., Galloway, S., Stephen, B., et al. (2011) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2011.2166091 · Cited by 21 · see citing works

Reinforcement learning based backstepping control of power system oscillations

Karimi, A., Eftekharnejad, S., & Feliachi, A. (2009) · Electric Power Systems Research · doi:10.1016/j.epsr.2009.05.005 · Cited by 21 · see citing works

Stability-certified reinforcement learning: A control-theoretic perspectivePreprint

Jin, M., & Lavaei, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.11505 · Cited by 21 · see citing works

Intelligent Control of Grid-Connected Microgrids: An Adaptive Critic-Based Approach

Khorramabadi, S. S., & Bakhshai, A. (2014) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2014.2331188 · Cited by 19 · see citing works

Combining a Stability and a Performance-Oriented Control in Power Systems

Glavić, M., Ernst, D., & Wehenkel, L. A. (2005) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2004.841146 · Cited by 19 · see citing works

Application of reinforcement learning for generating optimal control signal to the IPFC for damping of low-frequency oscillations

Younesi, A., Shayeghi, H., & Moradzadeh, M. (2017) · International Transactions on Electrical Energy Systems · doi:10.1002/etep.2488 · Cited by 16 · see citing works

Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation

Fan, J., & Li, W. (2019) · OpenBU (Boston University) · doi:10.48550/arxiv.1903.02526 · Cited by 15 · see citing works

Trajectory-Based Supplementary Damping Control for Power System Electromechanical Oscillations

Wang, D., Glavić, M., & Wehenkel, L. A. (2014) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2014.2314359 · Cited by 14 · see citing works

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

(2019) · doi:10.48550/arxiv.1907.09059 · Cited by 13 · see citing works

Antijamming Game Framework for Secure State Estimation in Power Systems

He, J., Chen, C., Zhu, S., et al. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2871933 · Cited by 12 · see citing works

Learning-coordinated fuzzy logic control of dynamic quadrature boosters in multi-machine power systems

Li, B. H., Wu, Q., Wang, P. Y., et al. (1999) · IEE Proceedings - Generation Transmission and Distribution · doi:10.1049/ip-gtd:19990426 · Cited by 11 · see citing works

A reinforcement learning based discrete supplementary control for power system transient stability enhancement

Glavić, M., Ernst, D., & Wehenkel, L. A. (2005) · Open Repository and Bibliography (University of Liège) · Cited by 10 · see citing works

Risk-Averse Transmission Path Selection for Secure State Estimation in Power Systems

He, J., Chen, C., Zhu, S., Yang, B., & Guan, X. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2879110 · Cited by 9 · see citing works

Automatic learning of sequential decision strategies for dynamic security assessment and control

Wehenkel, L. A., Glavić, M., Geurts, P., et al. (2006) · IEEE Power Engineering Society General Meeting · doi:10.1109/pes.2006.1708874 · Cited by 5 · see citing works

Real-Time Cascading Failures Prevention Through MAS Algorithm and Immune System Reinforcement Learning

Belkacemi, R., Babalola, A. A., & Zarrabian, S. (2017) · Electric Power Components and Systems · doi:10.1080/15325008.2016.1267280 · Cited by 3 · see citing works

Nested Reinforcement Learning Based Control for Protective Relays in Power Distribution SystemsPreprint

Wu, D., Zheng, X., Kalathil, D. M., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.10815 · Cited by 3 · see citing works

RLOC: Neurobiologically Inspired Hierarchical Reinforcement Learning Algorithm for Continuous Control of Nonlinear Dynamical SystemsPreprint

Abramova, E., Dickens, L., Kühn, D., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03064 · Cited by 2 · see citing works

Data-Driven Based Method for Power System Time-Varying Composite Load ModelingPreprint

Xie, J. H., Ma, Z., Wang, Z., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.02688

Tackling climate change with machine learning

Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works

Multi-agent hierarchical reinforcement learning for energy management

Jendoubi, I., & Bouffard, F. (2023) · Applied Energy · doi:10.1016/j.apenergy.2022.120500 · Cited by 123 · see citing works

A Microgrid Operation based on a Power Market Environment

Kim, H., & Kinoshita, T. (2009) · Journal of the Korean Institute of Illuminating and Electrical Installation Engineers · doi:10.5207/jieie.2009.23.11.061 · Cited by 1 · see citing works

Hierarchical Coordination of a Community Microgrid With AC and DC Microgrids

Che, L., Shahidehpour, M., Alabdulwahab, A., & Al‐Turki, Y. A. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2398853 · Cited by 471 · see citing works

Multi-agent-based microgrid cluster power coordinated autonomous control strategy

Shi, S., Wang, Y., Zhu, J., et al. (2022) · International Conference on Mechanisms and Robotics (ICMAR) · doi:10.1117/12.2652172

Microgrids: introduction and research problem descriptions

Xu, Y., Wang, Y., Zhang, C., et al. (2021) · Institution of Engineering and Technology eBooks · doi:10.1049/pbpo188e_ch2

Study on some key problems and technique related to microgrid

Wu, Z. (2010) · Power System Protection and Control · Cited by 24 · see citing works