Robotics and Artificial Intelligence in Energy Systems

One of the most important challenges in modern power engineering is ensuring the reliability, safety, and efficient operation of energy equipment amid increasing infrastructure complexity and growing energy consumption. In this context, robotics and artificial intelligence are becoming key tools for the digital transformation of the sector. They enable automated monitoring and maintenance of facilities, timely detection of faults and process abnormalities, and optimization of energy-system operating modes. Their use is especially important for equipment located in hard-to-access and hazardous areas, as well as for ensuring stable energy supply and efficient resource management. Particular attention in the session will be given to intelligent systems for equipment condition diagnostics and forecasting; autonomous robotic systems for inspection and maintenance of power transmission lines, substations, and generation facilities; and machine-learning algorithms for optimizing grid operating modes and demand management. The session will provide a platform for exchanging experience among researchers, developers, and practicing engineers, supporting the development of this promising field at the intersection of energy, automation, and artificial intelligence. Topics of interest include, but are not limited to:

1. Application of robots in energy systems

2. Intelligent control systems

3. Modeling of processes in energy systems using artificial intelligence

4. Development of key functional units of robotic systems

5. Robot machine-vision systems

Chairs:

Bobriakov Alexander Vladimirovich, National Research University “Moscow Power Engineering Institute” (MPEI), Russia 

Alexander V. Bobriakov is Head of the Department of Control and Intelligent Technologies and Director of the Information and Computing Center at the National Research University "Moscow Power Engineering Institute" (MPEI). He is also Director of the Center for Sectoral Information and Analytical Systems of the Ministry of Science and Higher Education of the Russian Federation. He is a recipient of the Government of the Russian Federation Prize in Science and Technology and has been awarded the titles "Honored Power Engineer of the Russian Federation" and "Honorary Worker of Higher Professional Education of the Russian Federation." He is a member of the Russian Association for Artificial Intelligence and a corresponding member of the Russian Engineering Academy. He has published more than 200 scientific works and has extensive experience in theoretical and applied research on modeling processes in complex technical, organizational-technical, and economic systems using artificial intelligence and intelligent data-processing methods and technologies, as well as in designing distributed information and analytical systems and their components for information processing, analysis, decision support, and implementation of various control strategies. 

Biao Wang, Chang'an University, China

Biao Wang, male, born in 1969, received his PhD in Control, Information and Systems from Moscow Power Engineering Institute. He is an associate professor in the Department of Automation, School of Electrical and Control Engineering, Chang'an University, and a master's supervisor. He is an overseas member of the Russian International Academy of Informatization, an overseas researcher at Moscow Power Engineering Institute, a member of the Chinese Association for Artificial Intelligence and its Science Popularization Committee, a member of the Chinese Association of Automation, and a committee member of the Process Control and Instrumentation Branch of the Shaanxi Association of Automation. He has more than ten years of experience in practical development and on-site debugging of industrial control systems. His main research interests include: System analysis and optimization, artificial intelligence and robot control, and process control theory and technology; System fault diagnosis and reliability; Security analysis and assessment of industrial control systems.