Early progress in the development of a radiometric PD location system

Neto, José; Macedo, E C; Batista, Mafalda; Cavalcanti, T. C. M.; Guedes, Edson Carvalho; Neto, José Sérgio da Rocha; Glover, Ian

doi:10.1109/i2mtc.2012.6229390

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Initially, to create a database of internal discharge in a test specimen in oil, a PD cell consisting of a cylindrical acrylic cell [47] was used. The PD cell had a pair of electrodes, one of which was connected to a high potential.…”

Section: Methodsmentioning

confidence: 99%

Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System

Carvalho,

da Costa,

Nobrega

et al. 2024

Sensors

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This paper addresses the critical challenge of detecting, separating, and classifying partial discharges in substations. It proposes two solutions: the first involves developing a signal conditioning system to reduce the sampling requirements for PD detection and increase the signal-to-noise ratio. The second approach uses machine learning techniques to separate and classify PD based on features extracted from the conditioned signal. Three clustering algorithms (K-means, Gaussian Mixture Model (GMM), and Mean-shift) and the Support Vector Machine (SVM) method were used for signal separation and classification. The proposed system effectively reduced high-frequency components up to 50 MHz, improved the signal-to-noise ratio, and effectively separated different sources of partial discharges without losing relevant information. An accuracy of up to 93% was achieved in classifying the partial discharge sources. The successful implementation of the signal conditioning system and the machine learning-based signal separation approach opens avenues for more economical, scalable, and reliable PD monitoring systems.

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Section: Methodsmentioning

confidence: 99%

Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System

Carvalho,

da Costa,

Nobrega

et al. 2024

Sensors

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“…The oscilloscope was a Rohde & Schwarz-RTA4004 (1 GHz and 5 GSa/s) configured for acquisition at 125 MSa/s on four channels, with 2.5 MSa per channel. Lastly, for PD generation, five test objects were used: a hydrogenerator bar with an applied nominal operating voltage of 6 kV; Phenolite dielectric discs with two and three internal cavities of 2 mm diameter submitted to 16 kV and 18 kV, respectively, both designed and made by [27] and submitted to a PD generation cell developed by [28]; potential transformer at 20 kV nominal voltage and; tip-to-tip electrodes immersed in oil, spaced by 4 cm and submitted to 12 kV.…”

Section: Methodsmentioning

confidence: 99%

Classification of Partial Discharge Sources in Ultra-High Frequency Using Signal Conditioning Circuit Phase-Resolved Partial Discharges and Machine Learning

Santos Júnior,

Serres,

Xavier

et al. 2024

Electronics

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This work presents a methodology for the generation and classification of phase-resolved partial discharge (PRPD) patterns based on the use of a printed UHF monopole antenna and signal conditioning circuit to reduce hardware requirements. For this purpose, the envelope detection technique was applied. In addition, test objects such as a hydrogenerator bar, dielectric discs with internal cavities in an oil cell, a potential transformer and tip–tip electrodes immersed in oil were used to generate partial discharge (PD) signals. To detect and classify partial discharges, the standard IEC 60270 (2000) method was used as a reference. After the acquisition of conditioned UHF signals, a digital signal filtering threshold technique was used, and peaks of partial discharge envelope pulses were extracted. Feature selection techniques were used to classify the discharges and choose the best features to train machine learning algorithms, such as multilayer perceptron, support vector machine and decision tree algorithms. Accuracies greater than 84% were met, revealing the classification potential of the methodology proposed in this work.

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“…Dois parafusos de nylon são usados para definir a distância entre os elétrodos que estão ligados à fonte de alimentação de alta tensão. Detalhes do gerador DP são dados em [11], [12].…”

Section: Modelo De Simulaçãounclassified

Simulação de Pulsos de Descargas Parciais para Sistemas de Detecção Radiométricos

Neto¹,

Carvalho²

2018

Anais De XXXVI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-Medição de Descargas Parciais (DP) é uma ferramenta de monitoramento do isolamento de alta tensão estabelecida e usada para facilitar a detecção de falhas incipientes em equipamentos de alta tensão. Um sistema radiométrico de monitoramento de descargas parciais de baixo custo é descrito para aplicação de monitoramento em tempo real do estado de conservação, gestão de ativos e otimização de operações no futuro Smart Grid. O sistema de monitoramento de DP proposto baseia-se em uma nova abordagem para a localização de fontes de DP que utiliza proporções de energia removendo a necessidade de sincronização entre os sensores, melhorando assim sua escalabilidade. A exigência de alta taxa de amostragem é também evitada pelo uso de um detector de envoltória. Para a avaliação proposta de sinais de DP, medições foram realizadas utilizando uma antena diskcone e um sistema comercial baseado no padrão IEC 60270. Estas medições, aqui relatadas, foram utilizadas para a concepção de um modelo de propagação que foi utilizado para viabilizar o projeto de um nó sensor radiométrico, levando em consideração as características de propagação de um sinal de descarga parcial.

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Early progress in the development of a radiometric PD location system

Cited by 6 publications

References 9 publications

Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System

Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System

Classification of Partial Discharge Sources in Ultra-High Frequency Using Signal Conditioning Circuit Phase-Resolved Partial Discharges and Machine Learning

Simulação de Pulsos de Descargas Parciais para Sistemas de Detecção Radiométricos

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