Statistical Significance of Wavelet Extracted Features in the Condition Monitoring of Ceramic Outdoor Insulators

Haiba, Ahmed S.; Gad, Ahmed; Eldebeikey, Soliman M.; Halawa, Mamdouh

doi:10.1109/eic43217.2019.9046590

Cited by 7 publications

(9 citation statements)

References 9 publications

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“…At both transmission and distribution voltage ratings, utilities have employed overhead lines that are supported by outdoor insulators. These insulators serve a crucial function of insulating the high voltage transmission line from the steel tower (Ahmed S. Haiba et al 2019). Although due to their nature operation in overhead transmission and distribution lines, outdoor ceramic insulators are subjected to various defects under operating and environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Analysis for Classification of Defected Ceramic Insulators

Haiba

Eliwa

2023

Preprint

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Partial discharge (PD) is a phenomenon that could occur in insulating materials when there is a localized breakdown of the electrical insulation. PD can lead to the formation of small arcs or sparks within the insulating material, which can cause damage and degradation to the insulator over time. In ceramic insulators, there are several factors that can cause PD including manufacturing defects, aging, and exposure to environmental conditions such as moisture and temperature extremes. As a result, detecting and monitoring PD in ceramic insulators is important for ensuring the reliability and safety of electrical systems that rely on these insulators. This can be done using various techniques which can provide information about the location, severity, and frequency of PD events. In this study, acoustic emission technique is introduced for PD detection and condition monitoring of defective ceramic insulators. A sequence of data processing techniques are performed on the captured signals to extract and select the most significant signatures for classification of defects in insulator strings. Moreover, Fourier transform analysis is adopted to be compared to the wavelet transform analysis. Artificial neural network (ANN) has been used to build an intelligent classifier for easily and accurately classification of defective insulators. The overall recognition rate of the classifier was obtained as 96.03% from discrete wavelet transform analysis and 88.65% from fast Fourier transform analysis. This obtained result indicates high accuracy and performance classification.

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Section: Introductionmentioning

confidence: 99%

“…Minimum amplitude of a signal refers to the lowest value of the amplitude that the signal reaches during its cycle.Mean of energy (m e ) is the energy mean value (Ahmed S Haiba et al 2019…”

mentioning

confidence: 99%

Neural Network Analysis for Classification of Defected Ceramic Insulators

Haiba

Eliwa

2023

Preprint

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“…As a result, monitoring of PD activities is very significant tool for evaluating the performance of the insulating system. Several experimental detection methods have been used on the basis of both electrical and non-electrical techniques for detection, analyzing, and estimation of PD activities in solid dielectrics as described in [7,8]. On the other hand, there are PD models that have been introduced to study the PD performance inside cavities in insulating materials.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Partial Discharge Measurement for Defected Solid Dielectrics

2022

Journal of Measurement Science and Applications (JMSA)

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“…Generally, the features of insulators in aerial images include texture [7] shape [8] color [9], gradient [10], and edge [11], among others. Thus, edge detection, morphology, scale-invariant features [12], and wavelet transformations [13,14] are usually employed to extract features and separate insulators from complicated backgrounds. Mathematical models have been established to detect insulators, as well.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference

Liu

et al. 2021

Electronics

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Automatic inspection of insulators from high-voltage transmission lines is of paramount importance to the safety and reliable operation of the power grid. Due to different size insulators and the complex background of aerial images, it is a difficult task to recognize insulators in aerial views. Most of the traditional image processing methods and machine learning methods cannot achieve sufficient performance for insulator detection when diverse background interference is present. In this study, a deep learning method—based on You Only Look Once (YOLO)—will be proposed, capable of detecting insulators from aerial images with complex backgrounds. Firstly, aerial images with common aerial scenes were collected by Unmanned Aerial Vehicle (UAV), and a novel insulator dataset was constructed. Secondly, to enhance feature reuse and propagation, on the basis of YOLOv3 and Dense-Blocks, the YOLOv3-dense network was utilized for insulator detection. To improve detection accuracy for different sized insulators, a structure of multiscale feature fusion was adapted to the YOLOv3-dense network. To obtain abundant semantic information of upper and lower layers, multilevel feature mapping modules were employed across the YOLOv3-dense network. Finally, the YOLOv3-dense network and compared networks were trained and tested on the testing set. The average precision of YOLOv3-dense, YOLOv3, and YOLOv2 were 94.47%, 90.31%, and 83.43%, respectively. Experimental results and analysis validate the claim that the proposed YOLOv3-dense network achieves good performance in the detection of different size insulators amid diverse background interference.

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Statistical Significance of Wavelet Extracted Features in the Condition Monitoring of Ceramic Outdoor Insulators

Cited by 7 publications

References 9 publications

Neural Network Analysis for Classification of Defected Ceramic Insulators

Neural Network Analysis for Classification of Defected Ceramic Insulators

Modeling and Simulation of Partial Discharge Measurement for Defected Solid Dielectrics

Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference

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