Application of HFCT and UHF Sensors in On-Line Partial Discharge Measurements for Insulation Diagnosis of  High Voltage Equipment

Álvarez, F.; Garnacho, Fernando; Ortego, Javier; Sánchez-Urán, Miguel Ángel

doi:10.3390/s150407360

Cited by 178 publications

(135 citation statements)

References 33 publications

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“…In transformers, capacitive and inductive coupling sensors are generally used. The bushing tap of the transformer is used as a capacitive coupling sensor, while the current is measured by mean of high frequency current transformer (HFCT) which is an inductive coupling sensor [56,61,[63][64][65][66]. The electrical measurements are very accurate and can provide information about the PD intensity, and possible determination of the defect type.…”

Section: Electrical Detection Of Pdsmentioning

confidence: 99%

Electrical-Based Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers

Fofana

Hadjadj

2016

Energies

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Abstract:The condition of the internal cellulosic paper and oil insulation are of concern for the performance of power transformers. Over the years, a number of methods have been developed to diagnose and monitor the degradation/aging of the transformer internal insulation system. Some of this degradation/aging can be assessed from electrical responses. Currently there are a variety of electrical-based diagnostic techniques available for insulation condition monitoring of power transformers. In most cases, the electrical signals being monitored are due to mechanical or electric changes caused by physical changes in resistivity, inductance or capacitance, moisture, contamination or aging by-products in the insulation. This paper presents a description of commonly used and modern electrical-based diagnostic techniques along with their interpretation schemes.

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Section: Electrical Detection Of Pdsmentioning

confidence: 99%

Electrical-Based Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers

Fofana

Hadjadj

2016

Energies

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“…: (i) dissolved gas analysis in oil (DGA), (ii) detection of the acoustic emission signals (AE), and (iii) detection of electromagnetic waves in different frequency bands (HF/VHF/UHF) [8,12,17,21,23,25,28,30]. Electromagnetic PD detection methods are already widely applied in diagnostics of gas insulated lines and substations (GIL/GIS), rotating machines, cables and medium voltage substations [1,13]. At present, intensive research is being done on the development, implementation, and standardization of electromagnetic methods (particularly the UHF method) in the diagnostics of power transformers [9].…”

Section: Wojciech Sikorski Cyprian Szymczak Krzysztof Siodła Filip Polakmentioning

confidence: 99%

Hilbert curve fractal antenna for detection and on-line monitoring of partial discharges in power transformers

Sikorski¹,

Szymczak²,

Siodła³

et al. 2018

Eksploatacja i Niezawodność – Maintenance and Reliability

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Science and Technology article citation info: IntroductionLarge power transformers are the most critical component in electric power systems, as they are essential in maintaining a reliable supply of electric energy. There are many factors which cause a transformer malfunction, but those, which can potentially lead to catastrophic failure are winding damages (due to short-circuit, lightning, and other over-voltages) and insulation system failure (moisture, thermal aging, partial discharges). The damage from a catastrophic transformer failure may run into tens of millions of dollars [16]. To avoid such a scenario, power utilities are moving towards continuous transformer condition monitoring, based on dissolved gas analysis and acoustic emission (AE) or electromagnetic (HF/VHF/UHF) partial discharge detection.According to the newest research results and analyses presented by the experts of the CIGRE Working Group A2.37, in the technical brochure 642: Transformer Reliability Survey, the main reason of breakdowns of high voltage power transformers is damage to the windings and the main insulation system [3]. Mechanical defects in the form of winding deformations (axial displacements and radial deformations) and deterioration of insulation properties associated with thermal aging processes [6], can lead to the initiation of the partial discharge (PD) phenomena occurrence.In recent years, in the electric power industry and research centres, a trend consisting in developing and implementing advanced

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“…At this moment, fault diagnosis is being carried out by experts with the aid of computers and advanced techniques, such as in the one demonstrated by Álvarez et al [42]. The ANN can be very attractive for both online and offline fault detections and diagnoses and can reduce the reliance on experts for fault interpretation, thereby reducing cost and visual implementation work.…”

Section: Impact Of the Artificial Neural Network Research On Practicmentioning

confidence: 99%

Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

et al. 2016

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Abstract:In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.

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Application of HFCT and UHF Sensors in On-Line Partial Discharge Measurements for Insulation Diagnosis of High Voltage Equipment

Cited by 178 publications

References 33 publications

Electrical-Based Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers

Electrical-Based Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers

Hilbert curve fractal antenna for detection and on-line monitoring of partial discharges in power transformers

Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

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