Detection, Measurement, and Classification of Partial Discharge in a Power Transformer: Methods, Trends, and Future Research

Mondal, Mithun; Kumbhar, Ganesh B.

doi:10.1080/02564602.2017.1335244

Cited by 46 publications

(28 citation statements)

References 113 publications

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“…One of the most challenging issues in classifying PD patterns according to the ageing state of the cable insulator is to extract informative features from PD measurements. Most of the existing researches on PD feature extraction is applied to PD pattern recognition for defect models classification in HV equipment [14,15,16,17,18,19,20,21] or PD-noise discrimination [3]. Therefore, feature extraction and selection techniques are not sufficiently investigated for ageing state recognition.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction and ageing state recognition using partial discharges in cables under HVDC

Morette

Ditchi

Oussar

2020

Electric Power Systems Research

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PD detection is an effective way to evaluate the degradation state of cable insulation. The extraction and selection of relevant features from PD raw data have been mostly investigated to recognize the types of insulation defects in HV equipment. In this study, two different feature extraction methods combined with supervised classification techniques are implemented for ageing state recognition of a polyethylene-insulated cable under HVDC conditions. For this purpose, an original experimental setup is implemented. Experiments are performed on a long length 100 m coaxial cable subjected to high electric fields. PD events are detected by direct coupling and collected with a digitizing oscilloscope. Feature extraction based on PD pulse shape parameters represented in time domain as well as wavelet decomposition coefficients are used separately as input variables of Support Vector Machines classifiers (SVMs). A feature selection method is implemented to design optimized SVM classifiers that attribute an ageing state to the cable insulation. The classification performance achieved with both feature extraction methods are presented and compared. The results show satisfactory recognition rates of two ageing states of cable insulation, up to 100% with a small subset of variables, particularly when features are extracted from wavelet decomposition of PD experimental data.

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Section: Introductionmentioning

confidence: 99%

Feature extraction and ageing state recognition using partial discharges in cables under HVDC

Morette

Ditchi

Oussar

2020

Electric Power Systems Research

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“…To date, several review works related to PD detection based on UHF measurement have been conducted in recent years in terms of signal processing [ 3 ], localization [ 14 , 15 ], and pattern recognition [ 3 ]. However, few types of research have drawn on the systematic review of UHF sensors applied in PD diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Application of UHF Sensors in Power System Equipment for Partial Discharge Detection: A Review

Chai

Phung

Mitchell³

2019

Sensors

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Condition monitoring of an operating apparatus is essential for lifespan assessment and maintenance planning in a power system. Electrical insulation is a critical aspect to be monitored, since it is susceptible to failure under high electrical stress. To avoid unexpected breakdowns, the level of partial discharge (PD) activity should be continuously monitored because PD occurrence can accelerate the aging process of insulation in high voltage equipment and result in catastrophic failure if the associated defects are not treated at an early stage. For on-site PD detection, the ultra-high frequency (UHF) method was employed in the field and showed its effectiveness as a detection technique. The main advantage of the UHF method is its immunity to external electromagnetic interference with a high signal-to-noise ratio, which is necessary for on-site monitoring. Considering the detection process, sensors play a critical role in capturing signals from PD sources and transmitting them onto the measurement system. In this paper, UHF sensors applied in PD detection were comprehensively reviewed. In particular, for power transformers, the effects of the physical structure on UHF signals and practical applications of UHF sensors including PD localization techniques were discussed. The aim of this review was to present state-of-the-art UHF sensors in PD detection and facilitate future improvements in the UHF method.

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“…Many contemporary research papers concern the issue of denoising AE signals. Different algorithms have been proposed, usually based on artificial neural networks, fuzzy logic or wavelet decomposition [23,24,25,26,27,28]. Unfortunately most of the researchers used AE signals emitted by artificial PD sources in a laboratory environment, and usually, no real-life scenario was presented to verify the proposed methods.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately most of the researchers used AE signals emitted by artificial PD sources in a laboratory environment, and usually, no real-life scenario was presented to verify the proposed methods. Identifying (or classifying) the source of PD using the AE method is another issue readily discussed in modern research [18,26,27,29,30,31]. Additionally, in this case, it is usually limited to several selected artificial PD defects, generated under laboratory conditions.…”

Section: Introductionmentioning

confidence: 99%

A Classification Method for Select Defects in Power Transformers Based on the Acoustic Signals

Kunicki

Wotzka

2019

Sensors

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Effective, accurate and adequately early detection of any potential defects in power transformers is still a challenging issue. As the acoustic method is known as one of the noninvasive and nondestructive testing methods, this paper proposes a new approach of the classification method for defect identification in power transformers based on the acoustic measurements. Typical application of acoustic emission (AE) method is the detection of partial discharges (PD); however, during PD measurements other defects may also be identified in the transformer. In this research, a database of various signal sources recorded during acoustic PD measurements in real-life power transformers over several years was gathered. Furthermore, all of the signals are divided into two groups (PD/other) and in the second step into eight classes of various defects. Based on these, selected classification models including machine learning algorithms were applied to training and validation. Energy patterns based on the discrete wavelet transform (DWT) were used as model inputs. As a result, the presented method allows one to identify with high accuracy, not only the selected kind of PD (1st step), but other kinds of faults or anomalies within the transformer being tested (2nd step). The proposed two-step classification method may be applied as a supplementary part of a technical condition assessment system or decision support system for management of power transformers.

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Detection, Measurement, and Classification of Partial Discharge in a Power Transformer: Methods, Trends, and Future Research

Cited by 46 publications

References 113 publications

Feature extraction and ageing state recognition using partial discharges in cables under HVDC

Feature extraction and ageing state recognition using partial discharges in cables under HVDC

Application of UHF Sensors in Power System Equipment for Partial Discharge Detection: A Review

A Classification Method for Select Defects in Power Transformers Based on the Acoustic Signals

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