ROBOTICS IN POWER INDUSTRY

Ural Federal University

This internship is devoted to the development and analysis of cyber-physical robotic systems application for diagnosing the technical condition of power plants’ high-voltage electric equipment and the development of an automated system for recognizing equipment faults based on machine learning algorithms.

The internship consists of four main stages:

  • Configuring robotic systems – a multi-rotor unmanned aerial vehicle (UAV) and analysis of possible flight missions, allowing to diagnose malfunctions and faults of high-voltage equipment for a real power facility in Russia.
  • Development and description of standard diagnostic programs for electric equipment of a real power facility, depending on the examination purpose and the UAV rated payload, taking into account various external conditions.
  • Analysis of modern mathematical approaches and the possibilities of technical diagnostics big data processing systems software implementation to solve the problem of analyzing the high-voltage equipment technical condition based on machine learning methods.
  • Development of a mathematical model of an intelligent automated system based on machine learning algorithms for collecting, storing and processing data obtained from UAVs to identify malfunctions and faults in high-voltage equipment.
  • The target audience of the course covers bachelor and master students, progressing in Computer Science, Power Engineering and Electronics.

The program of the internship is mostly based on the results of Erasmus+ KA2 ESSENCE CBHE ESSENCE project realization at Ural Federal University. The main of the program is based on the “Artificial intelligence and machine learning in Smart Energy Systems” discipline with special focus on mathematical apparatus used for computer vision systems implementation. The program inherits the best EU experience acquired by UrFU experts during Erasmus+ ESSENCE project and offers the real industry case as a graduation work, which is solved together with the leading industry experts. The tutors of the program use active teaching technologies, raising smart energy systems’ competences and favoring soft skills development. The program may be fully taught in distant mode.

More information about the program and admission procedure could be found here.