Using partial discharge as sample signal source to identify contamination moisture pattern in power transformer insulating oil

Veloso, Giscard Francimeire Cintra; Silva, L. E. Borges da; Noronha, Ismael; Torres, Germano Lambert

doi:10.1109/iecon.2010.5675511

Cited by 6 publications

(10 citation statements)

References 9 publications

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“…PD monitoring allows assessing the power transformer's condition, and several works have been developed in this area in recent years. Methods for PD monitoring include ultrahigh frequency (UHF) and dissolved gas analysis (DGA) and acoustic emission (AE) [4,5,9,10].…”

Section: Partial Discharge In a Power Transformermentioning

confidence: 99%

“…In this context, this paper aims to present a new approach to estimating the oil contamination by moisture in power transformer insulation based on the spectral kurtosis (SK) calculation of the acoustic signals of partial discharges (PDs). In this work, a piezoelectric acoustic sensor is employed to detect different levels of moisture in insulating oil, using the same experimental setup described in [5].…”

Section: Introductionmentioning

confidence: 99%

“…Motivation for this study comes from the fact that if a PD produces acoustic signals, it should be possible to find a relation between changes in the system acoustic response and the changes in the insulating oil moisture contents. Moreover, traditional moisture detection techniques, in general, involve removal of oil samples for analysis in a laboratory, which makes this process expensive, time consuming, and not so reliable [5]. Therefore, a noninvasive approach to detecting the moisture level in the oil would be a great contribution.…”

Section: Introductionmentioning

confidence: 99%

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Moisture estimation in power transformer oil using acoustic signals and spectral kurtosis

Leite

Veloso

Silva

et al. 2016

Meas. Sci. Technol.

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The aim of this paper is to present a new technique for estimating the contamination by moisture in power transformer insulating oil based on the spectral kurtosis analysis of the acoustic signals of partial discharges (PDs). Basically, in this approach, the spectral kurtosis of the PD acoustic signal is calculated and the correlation between its maximum value and the moisture percentage is explored to find a function that calculates the moisture percentage. The function can be easily implemented in DSP, FPGA, or any other type of embedded system for online moisture monitoring. To evaluate the proposed approach, an experiment is assembled with a piezoelectric sensor attached to a tank, which is filled with insulating oil samples contaminated by different levels of moisture. A device generating electrical discharges is submerged into the oil to simulate the occurrence of PDs. Detected acoustic signals are processed using fast kurtogram algorithm to extract spectral kurtosis values. The obtained data are used to find the fitting function that relates the water contamination to the maximum value of the spectral kurtosis. Experimental results show that the proposed method is suitable for online monitoring system of power transformers.

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Section: Partial Discharge In a Power Transformermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Moisture estimation in power transformer oil using acoustic signals and spectral kurtosis

Leite

Veloso

Silva

et al. 2016

Meas. Sci. Technol.

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“…Power transformers are one of the most expensive investments in electric power systems [6]. They are fundamental components of electric power systems, and their reliability is an important factor in the operation of the system [7]. The transformer oil is a good insulating material.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, insulation strength gets reduced that may result in partial discharge of transformer oil [8]. Model-based online detection method [6], partial discharge method [7], chromatography, radiofrequency method, infrared spectroscopy, neural network method [9], and so forth are some of the techniques developed for detection of moisture content of transformer oil.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Moisture in Transformer Oil Using Optical Fiber as Sensor

Laskar

Bordoloi

2013

Journal of Photonics

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This paper describes an optical fiber sensor and temperature sensor-based instrumentation system to measure the moisture content in transformer oil. The sensor system consists of (i) Diode Laser Source, (ii) a bare and bent multimode fiber as sensor probe, (iii) an LDR as detector, (iv) LM35-based temperature sensor, and (v) microcontroller system having a trained ANN for processing and calibration. The bare and bent optical fiber sensor and the temperature sensor LM35 are used to provide the measures of refractive index (RI) and temperature of a transformer oil sample. An ATmega32-microcontroller-based system with trained ANN algorithm has been developed to determine the moisture content of the transformer oil sample by sampling the readings of the bare bent optical fiber sensor and the temperature sensor.

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Multi-resolution analysis based data compression for power transformer protection

Aktaibi

Rahman

2014

2014 International Conference on Electrical Machines (ICEM)

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Using partial discharge as sample signal source to identify contamination moisture pattern in power transformer insulating oil

Cited by 6 publications

References 9 publications

Moisture estimation in power transformer oil using acoustic signals and spectral kurtosis

Moisture estimation in power transformer oil using acoustic signals and spectral kurtosis

Monitoring of Moisture in Transformer Oil Using Optical Fiber as Sensor

Multi-resolution analysis based data compression for power transformer protection

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