Detection of Incipient Faults in Transformers by Gas Analysis

Pugh, P. S.; Wagner, H. H.

doi:10.1109/aieepas.1961.4501008

Cited by 44 publications

(6 citation statements)

References 2 publications

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“…В работах [7,8] [13]. В свя-зи с этим для повышения точности результата ЭГХ анализа энергетических масел при определении в них содержания растворенных газов необходимо:…”

Section: анализ исследований и публикацийunclassified

Development of methods for the gas chromatogrphic determination of the content of dissolved components in the energy oils

Зайцев¹,

Кишневский²,

Савич³

2014

EEJET

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Section: анализ исследований и публикацийunclassified

Development of methods for the gas chromatogrphic determination of the content of dissolved components in the energy oils

Зайцев¹,

Кишневский²,

Савич³

2014

EEJET

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“…An electrical fault in a transformer generates dissolved gases at a much faster than normal rate. 9,10 The DGA of transformers can reveal the thermal and electrical stresses generated inside oil-immersed power transformers. A DGA can identify nine gases: hydrogen, oxygen, nitrogen, methane, ethane, ethylene, acetylene, carbon dioxide, and carbon monoxide.…”

Section: Dga Of Power Transformersmentioning

confidence: 99%

“…As the mineral oil slowly degrades, it produces certain gases dissolved in the oil under normal operations. An electrical fault in a transformer generates dissolved gases at a much faster than normal rate 9,10…”

Section: Introductionmentioning

confidence: 99%

Data mining for oil‐insulated power transformers: an advanced literature survey

Huang

Sun

2012

WIREs Data Min & Knowl

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Knowledge discovery in database and data mining (DM) have emerged as high profile, rapidly evolving, urgently needed, and highly practical approaches to use dissolved gas analysis (DGA) data to monitor conditions and faults in oil‐immersed power transformers. This study reviews different DM approaches to oil‐immersed power transformer maintenance by discussing historical developments and presenting state‐of‐the‐art DM methods. Relevant publications covering a broad range of artificial intelligence methods are reviewed. Current approaches to the latter method are discussed in the field of DM for oil‐immersed power transformers. In this paper, various DM approaches are discussed, including expert systems, fuzzy logic, neural networks, classification and decision, and hybrid intelligent‐based diagnostic systems that apply the DGA database. © 2012 Wiley Periodicals, Inc. This article is categorized under: Application Areas > Industry Specific Applications

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“…[6] Therefore, using various methods to solve transformer design optimization, presenting a detailed treatment of transformer selection and considering the environmental cost of the transformer are mandatory [6]. There are various transformer fault diagnosis techniques proposed in the literature for different types of faults [7] such as thermal faults [8,9], dielectric related faults [10] and mechanical faults. [11] As a modern transformer diagnostic technique, stochastic Petri Nets are proposed for modeling of fault diagnosis process of transformers.…”

Section: Introductionmentioning

confidence: 99%

Fault detection in the secondary side of electric arc furnace transformer using the primary side data

Marvasti

Samet

2013

Int. Trans. Electr. Energ. Syst.

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This paper presents a novel methodology for detecting fault conditions in the secondary side of electric arc furnace (EAF) transformer. The major focus of this study is to design a digital algorithm which uses solely the primary side current. Therefore, in this new methodology, only the primary side current is measured, and the fault presence in the secondary side is investigated. Here, there are two main objectives. The first objective is minimizing the fail-to-trip functioning probability, and the second one is minimizing the Mal-Trip operation probability. The fault characteristics are extracted using accurate simulation of EAFs installed in the Mobarakeh Steel Company (MSC) in Isfahan, Iran to fulfill the first objective. Three-phase field data of instantaneous current from the primary side of eight EAF transformers of MSC is also provided to evaluate performance of the proposed algorithm in the normal conditions and to achieve the second objective. The proposed algorithm is based on the sudden reduction of current harmonics in the fault conditions which is evaluated by a proposed special index which is called "the difference function" in this paper. Overall reliability assessment (dependability and security) of the proposed protective scheme demonstrates that even in this highly varying and unpredictable environment, the proposed algorithm is so precise and fast in fault detection. Figure 14. Harmonic components of one of the measured records in the normal conditions. a) Fundamental. b) Second. c) Third. d) Fourth.

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Detection of Incipient Faults in Transformers by Gas Analysis

Cited by 44 publications

References 2 publications

Development of methods for the gas chromatogrphic determination of the content of dissolved components in the energy oils

Development of methods for the gas chromatogrphic determination of the content of dissolved components in the energy oils

Data mining for oil‐insulated power transformers: an advanced literature survey

Fault detection in the secondary side of electric arc furnace transformer using the primary side data

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