Support for Decision-Making to Ensure Reliable Operation of Transformers as Part of a Responsible Power Facility

2022

IJECE

The Bayesian classifier is a priori the optimal solution for minimizing the total error in problems of statistical pattern recognition. The article suggests using the classifier as a regular tool to increase the reliability of defect recognition in power oil-filled transformers based on the results of the analysis of gases dissolved in oil. The wide application of the Bayesian method for solving tasks of technical diagnostics of electrical equipment is limited by the problem of the multidimensional distribution of random parameters (features) and the nonlinearity of classification. The application of a generalized feature of a defect in the form of a nonlinear function of the transformer state parameters is proposed. This simultaneously reduces the dimension of the initial space of the controlled parameters and significantly improves the stochastic properties of the random distribution of the generalized feature. A special algorithm has been developed to perform statistical calculations and the procedure for recognizing the current technical condition of the transformer using the generated decision rule. The presented research results illustrate the possibility of the practical application of the developed method in the conditions of real operation of power transformers.

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Section: The Main Theoretical Provisionsmentioning

confidence: 99%

An innovative method of fault detection in power transformers

2022

IJECE

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“…To ensure the reliability of the power supply system, the electrical equipment forming this system is monitored and diagnosed. power transformers (PT) are among the most important and expensive of this equipment [1]- [5]. There are many methods for diagnosing the technical condition of the PT, for example, dissolved gases (DGA), furan analysis, measurement of the polarization-depolarization current, partial discharge, frequency response analysis, infrared analysis.…”

Section: Introductionmentioning

confidence: 99%

Predicting the technical condition of the power transformer using fuzzy logic and dissolved gas analysis method

Boyarova

2022

IJECE

Power transformers are one of the most important and complex parts of an electric power system. Maintenance is performed for this responsible part based on the technical condition of the transformer using a predictive approach. The technical condition of the power transformer can be diagnosed using a range of different diagnostic methods, for example, analysis of dissolved gases (DGA), partial discharge monitoring, vibration monitoring, and moisture monitoring. In this paper, the authors present a digital model for predicting the technical condition of a power transformer and determining the type of defect and its cause in the event of defect detection. The predictive digital model is developed using the programming environment in LabVIEW and is based on the fuzzy logic approach to the DGA method, interpreted by the key gas method and the Dornenburg ratio method. The developed digital model is verified on a set of 110 kV and 220 kV transformers of one of the sections of the distribution network and thermal power plant in the Russian Federation. The results obtained showed its high efficiency in predicting faults and the possibility of using it as an effective computing tool to facilitate the work of the operating personnel of power enterprises.

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Digital Model for Predictive Assessment of a Power Transformer Technical Condition

SMART Automatics and Energy

2022