Ultrasound and Artificial Intelligence Applied to the Pollution Estimation in Insulations

Ferreira, Tarso Vilela; Germano, André Dantas; Costa, Edson G. da

doi:10.1109/tpwrd.2011.2178042

Cited by 22 publications

(12 citation statements)

References 5 publications

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“… Filtragem digital por um filtro FIR do tipo passafaixa de apenas 9 coeficientes, cuja resposta em frequência é apresentada na Fig.3 Note que os atributos extraídos são no domínio do tempo, não sendo necessárias operações complexas, como transformada de Fourier para obtenção de frequências [7]. Isso reduz significativamente o esforço computacional facilitando a etapa seguinte de um estudo como esse, que é a escolha de um microcontrolador para implementar o pré-processamento e o classificador.…”

Section: B Pré-processamentounclassified

“…Nas simulações computacionais realizadas para treinamento/teste, o primeiro procedimento a ser realizado foi a normalização do conjunto de dados em função da sua média e desvio padrão. Assim, um padrão normalizado pode ser obtido por (7) em que x i é o i-ésimo atributo de um padrão, bem como e são, respectivamente, a média e o desvio padrão também para o i-ésimo atributo.…”

Section: Normalização Dos Dadosunclassified

“…Por exemplo, em [6] uma RNA é usada como um estimador de velocidade para o motor de indução trifásico. Já em [7], uma rede neural artificial foi utilizada para realizar o diagnóstico de defeitos em isoladores de subestações e de linhas de transmissão/distribuição. Em outro trabalho relacionado são utilizadas redes neurais com o intuito de reconhecer padrões de formas de onda de descargas parciais, permitindo assim sua associação com o ponto de ocorrência do efeito corona [8].…”

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Corona Effect Detection in Energized Polymeric Insulators Using Machine Learning and Ultrasonic Emissions

Silva

Aquino

Neto

et al. 2018

IEEE Latin Am. Trans.

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The occurrence of corona effect is an important indication that an insulator probably has malfunction. Thus, the corona detection can help to diagnose problems in insulators, which might cause serious damage to the power distribution system, such as the sudden interruption of service. Machine learning techniques are used to classifying patterns into categories. This paper evaluated the application of MLP (multilayer perceptron) and SVM (support vector machines) for corona detection in high voltage polymeric insulators using ultrasonic emissions. To perform the capture of these data, a specific electronic circuit was developed in order to capture the ultrasonic signals tuned in the 40 kHz. The results indicate that both methods offer high generalizability and looks to be promising techniques for this type of problem, using ultrasonic frequencies as the data source.

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Section: B Pré-processamentounclassified

Section: Normalização Dos Dadosunclassified

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Corona Effect Detection in Energized Polymeric Insulators Using Machine Learning and Ultrasonic Emissions

Silva

Aquino

Neto

et al. 2018

IEEE Latin Am. Trans.

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“…Currently, researchers also employ other indirect methods (e.g., light, sound and electricity) to determine the pollution levels. Hyperspectral imaging, microwave radiation theory, infrared and visible light information fusion, ultraviolet sensors, light detection sensors and acoustic emission technology have been employed to establish insulator pollution level prediction models [8][9][10][11][12][13][14][15]. With respect to the direct detection of pollution composition, aside from commonly used material composition analysis methods (e.g., ion emission spectroscopy techniques, including ion chromatography, X-ray diffraction and inductive coupling), very few researchers have examined online detection methods for insulator pollution composition.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Pollution in High Voltage Insulators via Laser-Induced Breakdown Spectroscopy

Wang¹,

Lü²,

Wang³

et al. 2020

Molecules

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Surface pollution deposition in a high voltage surface can reduce the surface flashover voltage, which is considered to be a serious accident in the transmission of electric power for the high conductivity of pollution in wet weather, such as rain or fog. Accordingly, a rapid and accurate online pollution detection method is of great importance for monitoring the safe status of transmission lines. Usually, to detect the equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD), the pollution should be collected when power cut off and bring back to lab, time-consuming, low accuracy and unable to meet the online detection. Laser-induced breakdown spectroscopy (LIBS) shows the highest potential for achieving online pollution detection, but its application in high voltage electrical engineering has only just begun to be examined. In this study, a LIBS method for quantitatively detecting the compositions of pollutions on the insulators was investigated, and the spectral characteristics of a natural pollution sample were examined. The energy spectra and LIBS analysis results were compared. LIBS was shown to detect pollution elements that were not detected by conventional energy spectroscopy and had an improved capacity to determine pollution composition. Furthermore, the effects of parameters, such as laser energy intensity and delay time, were investigated for artificial pollutions. Increasing the laser energy intensity and selecting a suitable delay time could enhance the precision and relative spectral intensities of the elements. Additionally, reducing the particle size and increasing the density achieved the same results.

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“…20, No. 1 (2019) Mahdjoubi A, Zegnini B, Belkheir M.: Prediction of critical flashover voltage of polluted insulators … 50 polluted insulator like X-ray fluorescence technique [8]; and Ultrasound technique based on ultrasonic noise emissions [9].…”

Section: Introductionmentioning

confidence: 99%

Prediction of critical flashover voltage of polluted insulators under sec and rain conditions using least squares support vector machines (LS-SVM)

Abdelhalim¹,

Zegnini²,

Belkheiri³

2018

Diagnostyka

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This paper describes a methodology that was developed for the prediction of the critical flashover voltage of polluted insulators under sec and rain conditions least squares support vector machines (LS-SVM) optimization. The methodology uses as input variable characteristics of the insulator such as diameter, height, creepage distance, and the number of elements on a chain of insulators. The estimation of the flashover performance of polluted insulators is based on field experience and laboratory tests are invaluable as they significantly reduce the time and labour involved in insulator design and selection. The majority of the variables to be predicted are dependent upon several independent variables. The results from this work are useful to predict the contamination severity, critical flashover voltage as a function of contamination severity, arc length, and especially to predict the flashover voltage. The validity of the approach was examined by testing several insulators with different geometries. A comparison with the Grouping Multi-Duolateration Localization (GMDL) method proves the accuracy and goodness of LS-SVM. Moreover LS-SVMs give a good estimation of results which are validated by experimental tests.

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Ultrasound and Artificial Intelligence Applied to the Pollution Estimation in Insulations

Cited by 22 publications

References 5 publications

Corona Effect Detection in Energized Polymeric Insulators Using Machine Learning and Ultrasonic Emissions

Corona Effect Detection in Energized Polymeric Insulators Using Machine Learning and Ultrasonic Emissions

Analysis of Pollution in High Voltage Insulators via Laser-Induced Breakdown Spectroscopy

Prediction of critical flashover voltage of polluted insulators under sec and rain conditions using least squares support vector machines (LS-SVM)

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