Analysis on Multiple Acoustic and Electrical Emission of PD Signal Based on Signal to Noise Ratio (SNR) on Power Cable

Mohammad, W. N. A. W.; Rohani, Mohamad Nur Khairul Hafizi; Rosle, N.; Khalid, K. N.; Rosmi, A. S.; Abdullah, Andi Zulkifli; Jamil, Muhammad Kamran

doi:10.1109/icspc50992.2020.9305756

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…5, 6 and 7. This observation is highly consistent with the outcomes of references 44,47,48 , and 49 .…”

Section: Feature Extraction and Selectionsupporting

confidence: 91%

“…Normally, shape matching by visual inspection is applied to pick up the most proper mother wavelet. Several researchers worked on this point of selection of mother wavelet in case of power system transients such as acoustic emissions [44][45][46][47][48][49] . W. N. A. W. Mohammad et al 47 concluded that the forms of wavelets db, coif, sym, bior, and rbio are used since they are the most appropriate wavelets in the case of acoustic and electrical emission of PD signals.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

“…Several researchers worked on this point of selection of mother wavelet in case of power system transients such as acoustic emissions [44][45][46][47][48][49] . W. N. A. W. Mohammad et al 47 concluded that the forms of wavelets db, coif, sym, bior, and rbio are used since they are the most appropriate wavelets in the case of acoustic and electrical emission of PD signals. Safavian et al 48 concluded that the db4, coiflet, and b-spline were equally suitable in detecting power system transients.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

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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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“…5, 6 and 7. This observation is highly consistent with the outcomes of references 44,47,48 , and 49 .…”

Section: Feature Extraction and Selectionsupporting

confidence: 91%