Imagen de Portada

A Deep Reinforcement Learning Method for Multi-objective Transmission Switching

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Lin, Ding, Wang, Jianhui, Zhao, Tianqiao, Yue, Meng
Formato:	Preprint
Publicado:	2025
Materias:	Systems and Control
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Reinforcement Learning Based Symbolic Regression for Load Modeling
por: Lin, Ding, et al.
Publicado: (2025)

Dispatch-Aware Deep Neural Network for Optimal Transmission Switching
por: Kim, Minsoo, et al.
Publicado: (2025)

Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of Beamlines
por: Wang, Siyu, et al.
Publicado: (2024)

Diffusion Model-based Parameter Estimation in Dynamic Power Systems
por: Zhu, Feiqin, et al.
Publicado: (2024)

Multi-UAV Collaborative Trajectory Planning for Seamless Data Collection and Transmission
por: Wang, Rui, et al.
Publicado: (2024)

Multi-Agent Deep Reinforcement Learning for Multiple Anesthetics Collaborative Control
por: Li, Huijie, et al.
Publicado: (2025)

Risk-based approach to the Optimal Transmission Switching problem
por: Jeanson, Benoît, et al.
Publicado: (2025)

Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation
por: Meng, Bo, et al.
Publicado: (2025)

Tightening Quadratic Convex Relaxations for the AC Optimal Transmission Switching Problem
por: Guo, Cheng, et al.
Publicado: (2022)

Deep Reinforcement Learning-Based Decision-Making Strategy Considering User Satisfaction Feedback in Demand Response Program
por: Li, Xin, et al.
Publicado: (2025)

Optimal Transmission Switching: Improving Solver Performance Using Heuristics
por: Hinneck, Anton, et al.
Publicado: (2021)

Finite Horizon Multi-Agent Reinforcement Learning in Solving Optimal Control of State-Dependent Switched Systems
por: Zhou, Mi, et al.
Publicado: (2023)

Multi-agent Deep Reinforcement Learning for Distributed Load Restoration
por: Vu, Linh, et al.
Publicado: (2023)

Scalable Iterative Algorithm for Solving Optimal Transmission Switching with De-energization
por: Jeanson, Benoît, et al.
Publicado: (2025)

Optimal Transmission Switching and Busbar Splitting in Hybrid AC/DC Grids
por: Bastianel, Giacomo, et al.
Publicado: (2024)

Analysis of Discrete-Time Switched Linear Systems under Logic Dynamic Switchings
por: Zhang, Xiao, et al.
Publicado: (2022)

Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Map
por: Liu, Shunyu, et al.
Publicado: (2024)

Nonholonomic Narrow Dead-End Escape with Deep Reinforcement Learning
por: Xiong, Denghan, et al.
Publicado: (2025)

Optimal Control of Switched Systems Governed by Logical Switching Dynamics
por: Zhang, Xiao, et al.
Publicado: (2026)

A Review of Safe Reinforcement Learning Methods for Modern Power Systems
por: Su, Tong, et al.
Publicado: (2024)

AC-Informed DC Optimal Transmission Switching Problems via Parameter Optimization
por: Taheri, Babak, et al.
Publicado: (2024)

Utilizing Adversarial Training for Robust Voltage Control: An Adaptive Deep Reinforcement Learning Method
por: Chung, Sungjoo, et al.
Publicado: (2026)

On Reward-Balancing Methods for Reinforcement Learning
por: Baroncini, Simone, et al.
Publicado: (2026)

Partial Attention in Deep Reinforcement Learning for Safe Multi-Agent Control
por: Mohaya, Turki Bin, et al.
Publicado: (2026)

Human Balancing on a Log: A Switched Multi-Layer Controller
por: Zhao, Jiayi, et al.
Publicado: (2024)

A Reinforcement Learning-based Transmission Expansion Framework Considering Strategic Bidding in Electricity Markets
por: Kanazawa, Tomonari, et al.
Publicado: (2026)

Multi-Satellite Beam Hopping and Power Allocation Using Deep Reinforcement Learning
por: Xie, Xia, et al.
Publicado: (2025)

A Deep Learning-Based Method for Power System Resilience Evaluation
por: Wang, Xuesong, et al.
Publicado: (2025)

Novel Multi-objective Switched Model Predictive Control with Feasibility and Stability Guarantees
por: Niepötter, Elias, et al.
Publicado: (2025)

Sequential Multi-objective Multi-agent Reinforcement Learning Approach for Predictive Maintenance
por: Chen, Yan, et al.
Publicado: (2025)

Dispatch-Aware Deep Neural Network for Optimal Transmission Switching: Toward Real-Time and Feasibility Guaranteed Operation
por: Kim, Minsoo, et al.
Publicado: (2025)

A Distributed Primal-Dual Method for Constrained Multi-agent Reinforcement Learning with General Parameterization
por: Kahe, Ali, et al.
Publicado: (2024)

A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning
por: Yao, Fulong, et al.
Publicado: (2024)

Reliable Transmission of LTP Using Reinforcement Learning-Based Adaptive FEC
por: Chen, Liang, et al.
Publicado: (2025)

A Novel Multi-Objective Reinforcement Learning Algorithm for Pursuit-Evasion Game
por: Hu, Penglin, et al.
Publicado: (2025)

AoI-Aware Task Offloading and Transmission Optimization for Industrial IoT Networks: A Branching Deep Reinforcement Learning Approach
por: Chen, Yuang, et al.
Publicado: (2025)

Deep Reinforcement Learning Behavioral Mode Switching Using Optimal Control Based on a Latent Space Objective
por: Remman, Sindre Benjamin, et al.
Publicado: (2024)

Reinforcement Learning-Based Controlled Switching Approach for Inrush Current Minimization in Power Transformers
por: Valdivielso, Jone Ugarte, et al.
Publicado: (2025)

Nuclear Microreactor Control with Deep Reinforcement Learning
por: Tunkle, Leo, et al.
Publicado: (2025)

Cooperative Bearing-Only Target Pursuit via Multiagent Reinforcement Learning: Design and Experiment
por: Li, Jianan, et al.
Publicado: (2025)