Classification of Degraded Polymer Insulator Using Support Vector Machine

Din, Abdul Talib; Piah, M. A. M.; Abdullah, Abdul Rahim; Abdullah, F. S.

doi:10.1109/icpadm49635.2021.9493974

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…By this tool, the best validation performance is achieved at error 0.031838. In addition to that, the overall accuracy recognition rate is obtained as 88.647% as illustrated in gure (15).…”

Section: Arti Cial Neural Networkmentioning

confidence: 81%

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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“…By this tool, the best validation performance is achieved at error 0.031838. In addition to that, the overall accuracy recognition rate is obtained as 88.647% as illustrated in gure (15).…”

Section: Arti Cial Neural Networkmentioning

confidence: 81%

Neural Network Analysis for Classification of Defected Ceramic Insulators

Haiba

Eliwa

2023

Preprint

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“…In their study, Salem et al 40 utilized insulator diameter, height, creepage distance, form factor, and equivalent salt deposit density (ESDD) as input parameters to train a model that combined the Adaptive neuro fuzzy inference system (ANFIS) with ANN. Also, by establishing correlations between leakage current and weather conditions, A. Din et al 41 used the SVM technique to evaluate the leakage current for outdoor insulators. In another work, Saranya et al 42 put forward a novel approach for assessing the status and performance of outdoor insulators, which involves recognizing the arc faults of insulators through measurements of phasor angle.…”

mentioning

confidence: 99%

Artificial neural network analysis for classification of defected high voltage ceramic insulators

Haiba,

Eliwa Gad

2024

Sci Rep

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Partial discharge (PD) could 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. In this study, acoustic emission technique is introduced for PD detection and condition monitoring of defective ceramic insulators. A sequence of data processing techniques is performed on the captured signals to extract and select the most significant signatures for classification of defects in insulator strings. 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 at 96.03% from discrete wavelet transform analysis and 88.65% from fast Fourier transform analysis. This obtained result indicates high accuracy and performance classification. The outcomes of ANN were verified by SVM and KNN algorithms.

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Wavelet Extracted Features in the Condition Monitoring of HV Ceramic Insulators

Haiba

2023

Preprint

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The components of high voltage transmission lines such as towers, insulators, wires, and accessories are continuously subjected to severe environmental conditions. As a result, it is necessary to monitor their health condition to prevent any sudden interruption in the supplied load and to allocate the maintenance investments where they are highly needed. Defective discs of ceramic insulators essentially contain partial discharge (PD) activities; i.e., the presence of PD activities may characterize the insulator’s poor condition. The detection of radio frequency (RF) waves emitted from the PD activities is an emerging technique to monitor the insulator’s condition during its operation. In this paper, various artificial defects are introduced to the insulator discs and the corresponding RF signatures are captured using a high frequency sensor under the normal operating voltage. Several features are extracted from the captured signals after their decomposition using the discrete wavelet transform. The analysis of variance test has been adopted to evaluate the significance of each feature and level in identifying the defect type. This step facilitates the training of an intelligent classifier that will automatically distinguish the insulator strings that has to be replaced along the line.

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Classification of Degraded Polymer Insulator Using Support Vector Machine

Cited by 3 publications

References 15 publications

Neural Network Analysis for Classification of Defected Ceramic Insulators

Neural Network Analysis for Classification of Defected Ceramic Insulators

Artificial neural network analysis for classification of defected high voltage ceramic insulators

Wavelet Extracted Features in the Condition Monitoring of HV Ceramic Insulators

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