Statistical and neural network analysis of dissolved gases in power transformers

Thang, Ka Fei; Aggarwal, Ruchi; Esp, D.G.; McGrail, A.J.

doi:10.1049/cp:20000527

Cited by 23 publications

(10 citation statements)

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“…The results show that combining two or more sensors, using modern computer technology, and developing the corresponding data processing software, we can measure the content of two kinds of fault gases and can achieve significantly better detection performance than one conventional sensor with a single monitoring device [33]. The development of gas-sensor array technology includes two aspects, one is to develop integrated micro gas-sensor array using micro-manufacturing, micro-machining technology; the other is to improve the accuracy of a single gas identification and realize quantitative analysis mixed gas using multisensor information fusion technology.…”

Section: According To the Sensors That Collect The Signalsmentioning

confidence: 99%

Experimental Analysis of Modified CNTs-Based Gas Sensor

Tang¹,

Zhang²,

Xiao³

et al. 2017

Electrochemical Sensors Technology

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As a significant equipment in power system, the operation condition of transformers directly determines the safety of power system. Therefore, it has been an indispensable measure to detect and analyze the dissolved gases in transformers, aiming to estimate the early potential faults in oil-insulated transformers. In this chapter, the adsorption processes between modified carbon nanotubes (CNTs) (CNTs-OH, Ni-CNTs) and dissolved gases in transformers oil including C 2 H 2 , C 2 H 4 , C 2 H 6 , CH 4 , CO, and H 2 have been simulated based on the first principle theory. Meanwhile, the density of states (DOS), adsorption energy, charge transfer amount, and adsorption distance of adsorption process between CNTs and dissolved gases were calculated. Moreover, two kinds of sensors, mixed acid-modified CNTs and NiCl 2 -modified CNTs, are prepared to conduct the dissolved gases response experiment. Then, the gas response mechanisms were investigated. Finally, the results between response experiment and theoretical calculation were compared, reflecting a good coherence with each other. The CNTs gas sensors possess a relatively high sensitivity and fine linearity, and could be employed in dissolved gas analysis equipment in transformer.

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Section: According To the Sensors That Collect The Signalsmentioning

confidence: 99%

Experimental Analysis of Modified CNTs-Based Gas Sensor

Tang¹,

Zhang²,

Xiao³

et al. 2017

Electrochemical Sensors Technology

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“…The main drawbacks [6] of this conventional method are unable to predict the fault of a particular transformer owing to the lack of expert knowledge in them.…”

Section: Fig 31: Gases Presented In Transformer Oilmentioning

confidence: 99%

Failure Analysis of a Power Transformer Using Dissolved Gas Analysis – A Case Study

.¹

2014

IJRET

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“…Based on the review of literature, among others [21][22][23][24][25][26][27][28][29], and our own research work [30][31][32][33], authors suggested the use of a one-way multilayer neural network, which was implemented in Matlab software environment, for recognition of basic PD forms measured by the AE method. The structure applied in this work is a network type Feed -Forward Backpropagation Network (F -F BP), in which each neuron had a sigmoid activation function.…”

Section: Selection Of Architecture and Teaching Algorithm Of The Ann mentioning

confidence: 99%

Application possibilities of artificial neural networks for recognizing partial discharges measured by the acoustic emission method

Boczar

Borucki

Cichoń

et al. 2009

IEEE Trans. Dielect. Electr. Insul.

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The genesis of the research work presented in this paper constitutes the issue of the effective and efficient recognition of single-source one-time partial discharge forms that can occur in insulation systems of power transformers. The paper presents research results referring to the use of single-direction artificial neural networks for recognizing basic partial discharge forms that can occur in paper-oil insulation impaired by aging processes. The research work results presented show the recognition effectiveness of basic partial discharge forms depending on the descriptor of the analysis of the acoustic emission signal analysis. The detailed cognitive aim was selection of input parameters and an artificial neural network which would be the best, considering recognition effectiveness and processing time, and which could be used as a classifier in an expert diagnostic system making identification of partial discharges measured by using the acoustic method possible.Index Terms -Partial discharge, paper-oil insulation, acoustics emission method, artificial neuron network, power transformer.

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Statistical and neural network analysis of dissolved gases in power transformers

Cited by 23 publications

References 0 publications

Experimental Analysis of Modified CNTs-Based Gas Sensor

Experimental Analysis of Modified CNTs-Based Gas Sensor

Failure Analysis of a Power Transformer Using Dissolved Gas Analysis – A Case Study

Application possibilities of artificial neural networks for recognizing partial discharges measured by the acoustic emission method

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