Diagnostics of the power oil-filled transformer equipment of thermal power plants


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Аннотация

Problems concerning improvement of the diagnostics efficiency of the electrical facilities and functioning of the generation and distribution systems through the examples of the power oil-filled transformers, as the responsible elements referring to the electrical part of thermal power plants (TPP), were considered. Research activity is based on the fuzzy logic system allowing working both with statistical and expert information presented in the form of knowledge accumulated during operation of the power oil-filled transformer facilities. The diagnostic algorithm for various types of transformers, with the use of the intellectual estimation model of its thermal state on the basis of the key diagnostic parameters and fuzzy inference hierarchy, was developed. Criteria for taking measures allowing preventing emergencies in the electric power systems were developed. The fuzzy hierarchical model for the state assessment of the power oil-filled transformers of 110 kV, possessing high degree of credibility and setting quite strict requirements to the limits of variables of the equipment diagnostic parameters, was developed. The most frequent defects of the transformer standard elements, related with the disturbance of the isolation properties and instrumentation operation, were revealed after model testing on the real object. Presented results may be used both for the express diagnostics of the transformers state without disconnection from the power line and for more detailed analysis of the defects causes on the basis of the advanced list of the diagnostic parameters; information on those parameters may be received only after complete or partial disconnection.

Об авторах

D. Eltyshev

Perm National Research Polytechnic University

Автор, ответственный за переписку.
Email: eltyshevdk@gmail.com
Россия, Perm

N. Khoroshev

Perm National Research Polytechnic University

Email: eltyshevdk@gmail.com
Россия, Perm


© Pleiades Publishing, Inc., 2016

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