Development of intelligent computer devices for diagnostics and monitoring of electric power equipment based on identification measurements, methods of deep machine learning Deep Leaning and Big Data science

Priority direction:  Energy and mechanical engineering

Project leader: Koshekov K.T., Ph. D. 

The project: Ritter D.V., PhD., Kobenko V.Yu., PhD., Buoys, K.A., PhD., Kashevkin A.A., candidate of technical Sciences, PhD student, Kalanchevskaya N.A. master of science, PhD student, Latypov S.I., master of science, PhD student.

Terms of execution: 3 years.

Amount of funding: 62,000,000 tenge.

Project goal: Creation of computer devices and systems for monitoring and diagnostics, including software based on intelligent algorithms for collecting, primary processing and recognition of diagnostic and control signals of electric power equipment using the theory of identification measurements, computer and wireless infocommunication technologies in real time.

Expected result: Expected scientific and socio-economic impact:

- methodology for increasing energy savings through the introduction of intelligent technologies;

- methods and tools for diagnostics and monitoring of electric power equipment based on identification measurements of diagnostic and control signals, Deep Leaning and Big Data science and their reduction of environmental impact;

- improving the quality and speed of diagnostics and monitoring of high-voltage power equipment;

- getting new useful knowledge of energy saving through Deep Leaning in the electric power industry;

- development of information and communication technologies in the electric power industry.

Creation of experimental samples with subsequent testing at leading energy companies.

The target consumers of the results obtained are domestic and foreign enterprises for the production, transmission and distribution of electric energy, as well as organizations that develop equipment.

The use of identification measurements is ideal for solving problems of intelligent diagnostics of complex objects, creating fundamentally new equipment with processing of linguistic characteristics.

The use of Deep machine learning methods Deep Leaning and Big Data technologies will give researchers powerful tools for analyzing power equipment and developing new effective strategies for predicting performance. Obtaining European and Eurasian patents.

Project description: the Project is aimed at improving the efficiency of diagnostics and forecasting of electric power equipment failures by implementing a set of solutions that include Big Data tools and deep machine learning methods for analyzing informative signals (electrical, acoustic, vibration).

The project will result in the creation of intelligent computer devices and hardware and software complex for automated extraction of diagnostic information from informative signals.

Project objective: