Expert system for the assessment of power transformer insulation condition based on type-2 fuzzy logic systems

Flores, Wilfredo C.; Mombello, Enrique E.; Jardini, J.A.; Rattá, G.; Corvo, Antonio M.

doi:10.1016/j.eswa.2010.12.153

Cited by 51 publications

(25 citation statements)

References 13 publications

(22 reference statements)

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“…Systems reliant on rules and heuristics have a distinguished history of application in the condition monitoring of a wide variety of engineering scenarios: turbine generators [5], [6], power transformers [11], [12], and power system fault analysis [13]. The success in application of such systems reflects the advantages of knowledge-driven approaches, having the ability to provide clear conclusions and justification behind any decision support provided.…”

Section: A Related Workmentioning

confidence: 99%

Self-Tuning Routine Alarm Analysis of Vibration Signals in Steam Turbine Generators

et al. 2012

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This version is available at https://strathprints.strath.ac.uk/41099/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-This paper presents a self-tuning framework for the diagnosis of routine alarms in steam turbine generators utilizing a combination of inductive machine learning and knowledge-based heuristics. The techniques provide a novel basis for initializing and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine-specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm, and the applicability of systems using self-tuning techniques. The approaches discussed throughout are presented to provide useful diagnosis tools for the reliability and maintenance analysis of steam turbine generators.

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Section: A Related Workmentioning

confidence: 99%

Self-Tuning Routine Alarm Analysis of Vibration Signals in Steam Turbine Generators

et al. 2012

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“…Similar approach of data processing is described in [16] in which remaining service life of transformer in years is predicted based values of viscosity, resistivity, particle count, acidity and moisture content of oil. Another research [17] shows the possibility to combine measured values of moisture and acidity of oil, and the power factor of the winding to determine the type of transformer defect based on the physical and chemical condition of insulation in cases with contradicting input data values.…”

Section: General Description Of Oil Indicator Algorithmmentioning

confidence: 99%

Development of Indicators for Technical Condition Indexing of Power Transformers

Poiss¹,

Vītoliņa²,

Marks³

2018

Adv. sci. technol. eng. syst. j.

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“…Most researchers have focused on finding incipient faults in the systems before they occur, and today's technology allows the monitoring of power systems through many different sensors. In industry, there is a lot of monitoring equipment developed specifically to detect incipient faults that can occur in the systems [5][6][7][8]. However, it is important to compose evaluations based on data obtained from the system through various sensors.…”

Section: Introductionmentioning

confidence: 99%

A robust algorithm based on a failure-sensitive matrix for fault diagnosis of power systems: an application on power transformers

Biçen¹,

Aras²

2015

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, a robust algorithm for fault diagnosis of power system equipment based on a failure-sensitive matrix (FSM) is presented. The FSM is a dynamic matrix structure updated by multiple measurements (online) and test results (offline) on the systems. The algorithm uses many different artificial intelligence and expert system methods for adaptively detecting the location of faults, emerging failures, and causes of failures. In this algorithm, all data obtained from the power transformer, which have various nonlinear input and output parameters, are processed using the parallel matrix structure of the FSM to reach a global solution quickly. The parameters of a power transformer are used to verify the algorithm under 4 operating conditions simulated in the MATLAB-Simulink program. The obtained results show that the algorithm is convenient for determining incipient failures of a system that consists of multiple parameters.

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Expert system for the assessment of power transformer insulation condition based on type-2 fuzzy logic systems

Cited by 51 publications

References 13 publications

Self-Tuning Routine Alarm Analysis of Vibration Signals in Steam Turbine Generators

Self-Tuning Routine Alarm Analysis of Vibration Signals in Steam Turbine Generators

Development of Indicators for Technical Condition Indexing of Power Transformers

A robust algorithm based on a failure-sensitive matrix for fault diagnosis of power systems: an application on power transformers

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