An artificial neural network approach to transformer fault diagnosis

Zhang, Y.; Ding, Xuewen; Liu, Y.; Griffin, P.J.

doi:10.1109/61.544265

Cited by 292 publications

(61 citation statements)

References 3 publications

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“…The application of ANN makes possible to reduce considerably the laboratory experiment time while networks learn how to predict properties of insulation for duration longer than those of the tests thus constituting a tool making more economic the tests of high voltage in general. ANN method is more accurately applied to Dissolved Gas Analysis since the hidden relationships between fault types and dissolved gases can be recognized by ANN through training process [12][13][14][15].…”

Section: Artificial Neural Network Application To Dgamentioning

confidence: 99%

Review of Artificial Intelligence Techniques Application to Dissolved Gas Analysis on Power Transformer

Sharma¹,

Tiwari²,

Sood³

2011

IJCEE

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Abstract-Transformers are a critical part of an electrical utility's asset base. On-line monitoring and diagnostics is a useful tool to help operators to manage their assets and make decisions on continuing operation, maintenance or replacement. Dissolved Gas Analysis (DGA) is the heart of on-line monitoring as it is a well-established method of transformer diagnosis. DGA techniques are simple, inexpensive, and widely used to interpret gases dissolved due to the deterioration of the insulating oil of power transformers and hence to diagnosis, possibility of various type of faults in power transformer. Various diagnostic criteria based on gas analysis have been developed. In this paper, the application of many AI techniques have been presented such as Artificial Neural Network (AAN), Fuzzy Interface System (FIS), Genetic Algorithm (GA), Extended Relation Function (ERF), Bayesian Network (BN), Self Organizing Map (SOM) and Discrete Wavelet Network (WNs) Transforms, which can be used to increase the efficient and accurate diagnosis for off line and on line monitoring of power transformers.Index Terms-Power Transformer, Dissolve Gas Analysis, Artificial Intelligence Techniques. I. INTRODUCTIONPower transformers play an important role in both the transmission and distribution of electrical power system. It is very essential oil-insulated components in power systems and its operational state determines the safety of the whole power system. Failure of a transformer may cause long interruptions in power supply and require expensive repairs. An incipient fault in a transformer should be detected as early as possible, preventing the transformer from further deterioration. Diagnostic approaches can be divided into two groups: on-line and off-line. Transformer ageing process of insulating oil and cellulose materials has been monitored by many different techniques such as Dissipation factor, capacitance, Breakdown voltage of oil and paper, Degree of polymerization (DP), Total combustible Gases (TCG), furan analysis, Interfacial Tension (IFT) analysis etc. Dissolved Gas Analysis is most widely method to detect incipient faults in oil filled transformer and Electrical equipment. Dissolved gas analysis is made on the basis of the standard IEC60599 standards [1]. Several diagnosis methods, such as the Rogers and Duval Triangle methods, are available to identify the different types of fault occurring in service such as arcing, partial discharges, or hot spots. DGA includes detection, quantification and characterization of the Gases. These gases, called characteristic gases, include, Hydrogen (H2), Methane (CH4), Ethane (C2H6), Ethylene (C2H4) and Acetylene (C2H2), and Carbon Oxides such as CO and CO2. The nature and the amount of the individual component gases extracted from the oil may be indicative of the type and degree of abnormality. The DGA data provides information about the condition of the transformer and advance warning of developing faults, monitoring the rate of fault development, confirm the presence of faults, conve...

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Section: Artificial Neural Network Application To Dgamentioning

confidence: 99%

Review of Artificial Intelligence Techniques Application to Dissolved Gas Analysis on Power Transformer

Sharma¹,

Tiwari²,

Sood³

2011

IJCEE

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“…Collected vibration features can be integrated into a neural network that can represent the complicated nonlinear relationship mapping from the vibration features to the mechanical faults [1]. Moreover, the neural network is robust to noise 0.…”

Section: Introductionmentioning

confidence: 99%

The Application of Radial Basis Function (RBF) Neural Network for Mechanical Fault Diagnosis of Gearbox

Wang

2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. In this paper, the radial basis function (RBF) neural network is used for the mechanical fault diagnosis of a gearbox. We introduce the basic principles of the RBF neural network which is used for pattern classification and features a fast learning pace and strong nonlinear mapping capability; thus, it can be employed for fault diagnosis. The gearbox is a widely-used piece of equipment in engineering, and diagnosing mechanical faults is of great significance for engineers. A numerical example is presented to demonstrate the capability of the proposed method. The results indicate that the mechanical faults of a gearbox can be correctly diagnosed with a trained RBF neural network.

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“…A la fecha, los estudios de confiabilidad en los transformadores a nivel mundial se han centrado en temas puntuales como el análisis de gas disuelto en transformadores Wang(2012), (Pereira, 2012a), (Zhan, Member, Goulart, Falahi, & Rondla, 2015), entrenamiento de herramientas como redes neuronales Zhang(1996), (Kuznetsova, Li, Ruiz, & Zio, 2014), sistemas expertos con información limitada del comportamiento ciertos transformadores (Lin, Ling, & Huang, 1993), identificación variables de mantenimiento mediante lógica difusa (Arshad, Islam, & Khaliq, 2014), minería de datos para calidad de eventos (M. Guder 2014), falla de transformadores y métodos estadísticos (Soto, 2015), (Youssef, 2003), (Mkandawire, Ijumba, & Saha, 2015), (Mago, Valles, & Olaya, 2012), (Georgilakis & Kagiannas, 2014), (Ridwan & Talib, 2014), (Zompakis, Bartzas, & Soudris, 2015), (Zompakis et al, 2015), (Henao, Amaya, & Jaramillo, 2014), entre otros. La siguiente figura permite dimensionar el estudio de transformadores utilizando herramientas que permitan analizar grandes volúmenes de datos y alguna técnica inteligente para este fin.…”

Section: Introductionunclassified

Análisis De Eventos Sobre Transformadores De Distribución en Una Empresa Del Sector Eléctrico en Colombia

Guayasamin

Castrillón

Cano

2017

RCTA

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An artificial neural network approach to transformer fault diagnosis

Cited by 292 publications

References 3 publications

Review of Artificial Intelligence Techniques Application to Dissolved Gas Analysis on Power Transformer

Review of Artificial Intelligence Techniques Application to Dissolved Gas Analysis on Power Transformer

The Application of Radial Basis Function (RBF) Neural Network for Mechanical Fault Diagnosis of Gearbox

Análisis De Eventos Sobre Transformadores De Distribución en Una Empresa Del Sector Eléctrico en Colombia

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