Novel colour image encoding system combined with ANN for discharges pattern recognition on polluted insulator model

Ferrah, Imene; Chaou, A. K.; Maadjoudj, D.; Teguar, M.

doi:10.1049/iet-smt.2019.0297

Cited by 9 publications

(1 citation statement)

References 22 publications

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“…As a mainstream artificial intelligence algorithm, ANN has been widely used in the field of insulator defect classification and recognition and has a high recognition accuracy [33,34]. Therefore, under the condition of keeping the parameters of the hidden layer di mension, the number of hidden layers, the number of iterations, the optimizer, the activa tion function, and the objective function consistent, the ANN and the MMSAE network proposed in this paper are used to diagnose ultrasonic, electromagnetic wave spectrum and acoustic-electrical fusion, respectively.…”

Section: Comparison and Analysis Of Mmsae And Ann Resultsmentioning

confidence: 99%

Composite Insulator Defect Identification Method Based on Acoustic–Electric Feature Fusion and MMSAE Network

et al. 2023

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Aiming to solve the partial discharge problem caused by defects in composite insulators, most existing live detection methods are limited by the subjectivity of human judgment, the difficulty of effective quantification, and the use of a single detection method. Therefore, a composite insulator defect diagnosis model based on acoustic–electric feature fusion and a multi-scale perception multi-input of stacked auto-encoder (MMSAE) network is proposed in this paper. Initially, during the withstanding voltage experiment, the electromagnetic wave spectrometer and ultrasonic detector were used to collect and process the data of six types of composite insulator samples with artificial defects. The electromagnetic wave spectrum, ultrasonic power spectral density, and n-S map were then obtained. Then, the network architecture of MMSAE was built by integrating a stacked auto-encoder and multi-scale perception module; the feature extraction and fusion methods of the electromagnetic wave spectrum and ultrasonic signal were investigated. The proposed method was used to diagnose test samples, and the diagnostic results were compared to those obtained using a single input source and the artificial neural network (ANN) method. The results demonstrate that the detection accuracy of acoustic–electric feature fusion is greater than that of a single feature; the accuracy of the proposed method is 99.17%, which is significantly higher than the accuracy of the conventional ANN method. Finally, composite insulator defect diagnosis software based on PYQT5 and Keras was developed. Ten 500 kV aging composite insulators were used to validate the effectiveness of the proposed method and design software.

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Section: Comparison and Analysis Of Mmsae And Ann Resultsmentioning

confidence: 99%