Fibre-optic sensors for partial discharge-generated ultrasound in elastomeric high-voltage insulation materials

2013 ◽  
Author(s):  
P. Rohwetter ◽  
W. Habel
Keyword(s):  
High Voltage ◽  
Partial Discharge ◽  
Fibre Optic ◽  
Insulation Materials ◽  
High Voltage Insulation

scholarly journals High electron affinity: a guiding criterion for voltage stabilizer design

2015 ◽  
Vol 3 (14) ◽  
pp. 7273-7286 ◽  
Author(s):  
Markus Jarvid ◽  
Anette Johansson ◽  
Villgot Englund ◽  
Angelica Lundin ◽  
Stanislaw Gubanski ◽  
...  
Keyword(s):  
High Voltage ◽  
Electron Affinity ◽  
Dielectric Strength ◽  
Voltage Stabilizer ◽  
High Electron ◽  
Insulation Materials ◽  
High Electron Affinity ◽  
High Voltage Insulation

A high electron affinity is found to correlate with the ability of voltage-stabilizer additives to improve the dielectric strength of cross-linked polyethylene, which is a highly desirable for high-voltage insulation materials.

scholarly journals Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3886
Author(s):  
Marek Florkowski
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Anomaly Detection ◽  
Optical Flow ◽  
High Voltage ◽  
Partial Discharge ◽  
Electrical Power System ◽  
Deep Convolutional Neural Networks ◽  
High Voltage Insulation ◽  
Discharge Patterns

In the resilient and reliable electrical power system, the condition of high voltage insulation plays a crucial role. In the field of high voltage insulation integrity, the partial discharge (PD) inception and development trends are essential for assessment criteria in diagnostics systems. The observed trend to employ more and more sophisticated algorithms with machine learning features and artificial intelligence (AI) elements is observed everywhere. The classification and identification of features in PD images is perceived as a critical requirement for an effective high voltage insulation diagnosis. In this context, techniques allowing for anomaly detection, trends observation, and feature extraction in partial discharge patterns are important. In this paper, the application of few algorithms belonging to image processing, machine learning and optical flow is presented. The feature extraction refers to image segmentation and detection of coherent forms in the images. The anomaly detection algorithms can trigger early detection of the trend changes or the appearance of a new discharge form, and hence are suitable for PD monitoring applications. Anomaly detection can also handle transients and disturbances that appear in the PD image as an indication of an abnormal state. The future monitoring systems should be equipped with trend evolution algorithms. In this context, two examples of insulation aging and application of PD-based monitoring are shown. The first one refers to deep convolutional neural networks used for classification of deterioration stages in high voltage insulation. The latter one demonstrates application of optical flow approach for motion detection in partial discharge images. The motivation for the research was the strive to machine-controlled pattern analysis, leading towards intelligent PD-based diagnostics.

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