Condition Assessment of High Voltage Insulation in Power System Equipment

James, R. E.; Su, Qi

doi:10.1049/pbpo053e

Cited by 79 publications

(48 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Energies 2017, 10, 1060 3 of 20 In this paper, ANN and FL are considered because research findings indicate that both are techniques already integrated for commercial services in PD recognition [27]. PD faults of interest include single and multiple voids in PET and surface discharges on pressboard in oil commonly used as insulation for oil-immersed transformers.…”

Section: The Pd Detection Systemmentioning

confidence: 99%

Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources

et al. 2017

View full text Add to dashboard Cite

This paper compared the capabilities of the artificial neural network (ANN) and the fuzzy logic (FL) approaches for recognizing and discriminating partial discharge (PD) fault classes. The training and testing parameters for the ANN and FL comprise statistical fingerprints from different phase-amplitude-number (φ-q-n) measurements. Two PD fault classes considered are internal discharges in voids and surface discharges. In the void class, there are single voids, serial voids and parallel voids in polyethylene terephthalate (PET), while the surface discharge class comprises four different surface discharge arrangements on pressboard in oil at different voltages and angular positioning of the ground electrode on the respective pressboards. Previously, the ANN and FL have been investigated for PD classification, but there is no work reported in the literature that compares their performance, specifically when applied for real time PD detection problem. As expected, both the ANN and FL can recognize PD defect classes, but the results show that the ANN appears to be more robust as compared to the FL, but these conclusions required to be further investigated with complex PD examples. Finally, both the ANN and FL were assessed as practical PD classification. Despite of the limitations of the ANN, it is concluded that the ANN is better suited for practical PD recognition because of its ability to provide accurate recognition values and the severity level of PD defects.

show abstract

Section: The Pd Detection Systemmentioning

confidence: 99%

Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources

et al. 2017

View full text Add to dashboard Cite

show abstract

“…In the event of a contingency, the risk component of the transformer includes loss of body risk due to failure, and system association risk due to equipment failure or outage [5][6][7]. The calculation formula of transformer comprehensive risk is shown in equation (1).…”

Section: Quantitative Solution Of Transformer Comprehensive Risk Basementioning

confidence: 99%

Dynamic Comprehensive Risk Assessment of Transformers Considering Operating Conditions and Associated Risks

Zhou¹,

Li²,

Ouyang³

2017

dtcse

View full text Add to dashboard Cite

A method for calculating transformer comprehensive risk is proposed according to the operating conditions and associated risks of transformers in this paper. Based on the contingency analysis, a method for dynamic comprehensive evaluation of transformers is established. Considering the temperature rise of transformer winding and oil temperature, a life aging model based on hot spot temperature rise is constructed, then the accelerated deterioration factor is obtained. Based on the contingency analysis, a method for dynamic comprehensive evaluation of transformers is obtained. Collecting the failure rate of the transformer and the loss associated with the equipment is used to estimate the associated risk of the transformer. Finally, under the premise of operating condition correction, the quantitative assessment of transformer comprehensive dynamic risk is given based on the transformer risk loss and the associated risk loss. The effectiveness and reliability of the proposed method are demonstrated by the analysis of an example.

show abstract

“…Due to many advantages of the online CM, the capability of the ANN to be applied to online monitoring can be examined. Reviewing the literature [41] shows that online CM systems (e.g., PD measurement) have four main parts, i.e., sensors, data acquisition, fault detection and diagnosis. The sensors usually detect the fault and convert a physical quantity to an electrical signal.…”

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

View full text Add to dashboard Cite

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.

show abstract

Condition Assessment of High Voltage Insulation in Power System Equipment

Cited by 79 publications

References 0 publications

Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources

Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources

Dynamic Comprehensive Risk Assessment of Transformers Considering Operating Conditions and Associated Risks

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

Contact Info

Product

Resources

About