Application of ANFIS and ANN for Partial Discharge Localization in Oil Through Acoustic Emission

Hashim, Ahmad Hafiz Mohd; Azis, Norhafiz; Jasni, Jasronita; Radzi, Mohd Amran Mohd; Kozako, Masahiro; Jamil, Mohamad Kamarol Mohd; Yaakub, Zaini

doi:10.1109/tdei.2023.3264958

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…Secondly, transformers operate in environments with numerous interference signals, resulting in low signal-to-noise ratios (SNR) for the signals measured by the sensors and large localization errors. Especially for partial discharge signals occurring within transformer windings, even in ideal environments, PZT can experience difficulty capturing this type of discharge signal [1,[14][15][16][17]. With the increasing application of optical technology in partial discharge detection, fiber-optic ultrasonic sensors have emerged as a novel method for detecting partial discharges.…”

Section: Introductionmentioning

confidence: 99%

Localization of Dual Partial Discharge in Transformer Windings Using Fabry–Pérot Optical Fiber Sensor Array

Liu,

Du,

Shi

et al. 2024

Energies

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The power transformer is one of the most critical core devices for energy exchange in power systems, and its safe and stable operation is directly related to the reliability of the power grid. Partial discharge is the main cause of insulation degradation and failure of high-voltage electrical equipment. Online monitoring and accurate localization of partial discharge can provide information on the aging of power equipment, which is of great value for improving the safe operation and maintenance of the power grid. Internal dual partial discharge in transformer windings is a more complex type of fault. Since it is located inside the windings, the signal is attenuated and distorted, making it difficult for traditional monitoring methods to capture such partial discharge signal The Fabry–Perot optical fiber sensor is an ultrasonic detection method that can be built into the transformer interior. This sensor has high sensitivity and a small size, enabling flexible placement at different locations inside the transformer for precise partial discharge detection. Especially for the narrow space inside the high and low voltage windings, F–P sensors can form an array, utilizing the array’s directivity to locate the fault points. In this study, an ultrasonic detection system based on the F–P optical fiber sensor array was developed. The system utilizes a directional cross-localization algorithm based on the multiple signal classification (MUSIC) algorithm to accurately locate dual partial discharge sources. This partial discharge detection system was applied to a 35 kV single-phase transformer, enabling the localization of dual partial discharge sources within the high and low voltage windings. Combined with experimental results, this method exhibits high localization accuracy and is particularly suitable for detecting partial discharge phenomena that occur within or between transformer windings.

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Section: Introductionmentioning

confidence: 99%

Localization of Dual Partial Discharge in Transformer Windings Using Fabry–Pérot Optical Fiber Sensor Array

Liu,

Du,

Shi

et al. 2024

Energies

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“…Traditional methods of PD recognition are based on years of expert experience, practice, and other relevant knowledge, which are too subjective and have significant limitations. Currently, researchers have proposed some artificial intelligence algorithms to improve the precision of PD recognition such as artificial neural network (ANN) [4][5], back-propagation neural network (BPNN) [6][7], support vector machine (SVM) [8] and deep learning algorithm [9][10], etc. However, due to GIS PD online detection technology still needing to be optimized, and measured data collection is relatively difficult, resulting in the lack of sample data available for PD recognition.…”

Section: Introductionmentioning

confidence: 99%

GIS Partial Discharge Pattern Recognition Based on IWOA-SVM Algorithm

Wang,

Fang,

Kou

et al. 2024

J. Phys.: Conf. Ser.

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To effectively recognize partial discharge (PD) types in gas-insulated switchgear (GIS) equipment, a novel PD pattern recognition method is proposed. This method leverages an improved whale optimization algorithm (IWOA) to optimize the support vector machine (SVM). Firstly, typical PD defects were simulated in the GIS chamber. The PRPS results demonstrated significant differences among four PD defects. Secondly, multiple feature values of PD results were calculated for feature extraction. Finally, the IWOA-SVM algorithm was employed for the recognition of PD patterns in GIS. Experimental results indicate that the IWOA-SVM algorithm can effectively recognize different PD types and the predicted accuracy rate can reach 99.1667%, which is much higher than SVM and WOA-SVM algorithms, providing information to construction units for maintenance.

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Partial Discharge Localization Model in Power Transformer with Fingerprinting Technique

Chaisang

Tiengthong

Maw

et al. 2023

2023 9th International Conference on Engineering, Applied Sciences, and Technology (ICEAST)

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Application of ANFIS and ANN for Partial Discharge Localization in Oil Through Acoustic Emission

Cited by 5 publications

References 20 publications

Localization of Dual Partial Discharge in Transformer Windings Using Fabry–Pérot Optical Fiber Sensor Array

Localization of Dual Partial Discharge in Transformer Windings Using Fabry–Pérot Optical Fiber Sensor Array

GIS Partial Discharge Pattern Recognition Based on IWOA-SVM Algorithm

Partial Discharge Localization Model in Power Transformer with Fingerprinting Technique

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