Abstract
The research is devoted to the development of a fuzzy model for assessing the technical condition of power oil transformers based on the DGA. The parametric identification of optimal values of membership functions of fuzzy terms for linguistic variables is carried out to increase the reliability and objectivity of fault identification. For this purpose, it is proposed to use the theory of fuzzy sets, the nonlinear optimization method. A comparative analysis of the fuzzy simulation results for the technical condition with the fault diagnostic results on existing power transformers has confirmed high efficiency. The diagnostic accuracy of the adapted fuzzy model for the technical condition assessment of power transformers is 97 %, which is acceptable in the power transformers diagnostic. The developed model will be used for further research on the development of an algorithm for making effective decisions regarding the operation strategy of power transformers and preventive control of the subsystem operation of electric power systems. The obtained results of the fuzzy simulation for the technical condition assessment of power transformers give grounds to assert regarding the possibility of implementation in software of operation risk analysis of electric power systems for power supply companies
Highlights
The analysis of operating conditions of modern power systems shows a steady increase in the accident rate [1]
This review shows that various fuzzy-logic techniques for the power transformer fault detection have been developed in order to reduce operating costs, enhance operational reliability and improve power and services of customers [19]
The aim of the present research is to develop the model of technical condition assessment of power transformers using the method, which would help to adapt the model to real operation conditions of power transformers of electric power systems
Summary
The analysis of operating conditions of modern power systems shows a steady increase in the accident rate [1] This is primarily due to an increase in the share of electrical equipment failures, low rates of replacement, complicated weather conditions and working conditions of operational personnel. The mechanical strength of paper insulation will decrease and short circuit events like this can cause the ultimate transformer failure. Energy-saving technologies and equipment are often obtained on the basis of limited statistical data and subjective information of repair from maintenance personnel In this regard, the development of mathematical models for diagnosing the technical condition of power transformers and adapting to real operation conditions in power systems is an actual problem
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