Acoustic Fault Detection Technique for High-Power Insulators

Park, Kyu-Chil; Motai, Yuichi; Yoon, Jong Rak

doi:10.1109/tie.2017.2716862

Cited by 84 publications

(35 citation statements)

References 17 publications

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“…Contaminated insulators do not represent a system failure that ensures the component replacement action, however, it may be the indication that a failure will develop on-site [29]. In this way, through AI applied for forecasting purposes, it is possible to predict the development of failures based on the ultrasound signal from a contaminated insulator.…”

Section: Overview Of Insulators Conditionmentioning

confidence: 99%

Fault detection in insulators based on ultrasonic signal processing using a hybrid deep learning technique

Stefenon

Freire

Meyer

et al. 2020

IET Science, Measurement &Amp; Technology

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Identifying problems in insulators is a task that requires the experience of the operator. Contaminated insulators generally do not represent a system failure, however, due to higher surface conductivity, they may suffer from electrical discharges and may result in irreversible failures. The identification of possible failures in inspections can help to forecast faults to improve reliability in the power grid. Based on this need, this article presents a study on fault prediction in distribution insulators, through a laboratory evaluation in a contaminated insulator, where 13.8 kV (root mean square) was applied considering an ultrasound detector connected to a computer for data set acquisition. In the sequence, a time series prediction, using a hybrid deep learning technique defined as wavelet long short-term memory (LSTM), was performed. The hybrid LSTM was proposed considering feature extraction through the wavelet energy coefficient. Finally, for a complete evaluation, deeper LSTM layers were included, and both the training method and the hardware configuration were evaluated. The wavelet LSTM algorithm showed interesting accuracy results when compared to classic prediction algorithms like the non-linear autoregressive exogenous model.

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Section: Overview Of Insulators Conditionmentioning

confidence: 99%

Fault detection in insulators based on ultrasonic signal processing using a hybrid deep learning technique

Stefenon

Freire

Meyer

et al. 2020

IET Science, Measurement &Amp; Technology

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show abstract

“…In [17,28], the Otsu algorithm is applied to obtain the insulator regions. Subsequently, the insulator contours are achieved by the wavelet modulus maximum In traditional manual inspection, people have to walk along paths near high-voltage transmission lines, and then check each insulator status by using various types of instruments, such as audition sensors [7], infrared imagers [8][9][10], cameras [11], and ultraviolet imagers [12]. However, the traditional manual method is inefficient and not feasible in practice as high-voltage transmission lines are usually built in complex surroundings containing forests and lakes.…”

Section: Appl Sci 2019 9 X For Peer Review 2 Of 22mentioning

confidence: 99%

A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

et al. 2019

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Insulator faults detection is an important task for high-voltage transmission line inspection. However, current methods often suffer from the lack of accuracy and robustness. Moreover, these methods can only detect one fault in the insulator string, but cannot detect a multi-fault. In this paper, a novel method is proposed for insulator one fault and multi-fault detection in UAV-based aerial images, the backgrounds of which usually contain much complex interference. The shapes of the insulators also vary obviously due to the changes in filming angle and distance. To reduce the impact of complex interference on insulator faults detection, we make full use of the deep neural network to distinguish between insulators and background interference. First of all, plenty of insulator aerial images with manually labelled ground-truth are collected to construct a standard insulator detection dataset ‘InST_detection’. Secondly, a new convolutional network is proposed to obtain accurate insulator string positions in the aerial image. Finally, a novel fault detection method is proposed that can detect both insulator one fault and multi-fault in aerial images. Experimental results on a large number of aerial images show that our proposed method is more effective and efficient than the state-of-the-art insulator fault detection methods.

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“…The method of manual inspection can identify obvious foreign objects or damage; however, for small breakages and cracks, detection efficiency and accuracy are difficult to guarantee. Kyu-Chil Park et al used the sound fault detection method to classify fault state insulators by taking into account the total noise and the number of 120 Hz harmonic components [ 2 , 3 ]; however, this method is restricted by the distance used and the accuracy of the detection. Accordingly, it cannot be applied to online testing, and it is mostly used in the production of insulators.…”

Section: Introductionmentioning

confidence: 99%

Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology

Zhao¹,

Jing²,

Wang³

et al. 2021

Entropy

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A porcelain insulator is an important part to ensure that the insulation requirements of power equipment can be met. Under the influence of their structure, porcelain insulators are prone to mechanical damage and cracks, which will reduce their insulation performance. After a long-term operation, crack expansion will eventually lead to breakdown and safety hazards. Therefore, it is of great significance to detect insulator cracks to ensure the safe and reliable operation of a power grid. However, most traditional methods of insulator crack detection involve offline detection or contact measurement, which is not conducive to the online monitoring of equipment. Hyperspectral imaging technology is a noncontact detection technology containing three-dimensional (3D) spatial spectral information, whereby the data provide more information and the measuring method has a higher safety than electric detection methods. Therefore, a model of positioning and state classification of porcelain insulators based on hyperspectral technology is proposed. In this model, image data were used to extract edges to locate cracks, and spectral information was used to classify the surface states of porcelain insulators with EfficientNet. Lastly, crack extraction was realized, and the recognition accuracy of cracks and normal states was 96.9%. Through an analysis of the results, it is proven that the crack detection method of a porcelain insulator based on hyperspectral technology is an effective non-contact online monitoring approach, which has broad application prospects in the era of the Internet of Things with the rapid development of electric power.

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Acoustic Fault Detection Technique for High-Power Insulators

Cited by 84 publications

References 17 publications

Fault detection in insulators based on ultrasonic signal processing using a hybrid deep learning technique

Fault detection in insulators based on ultrasonic signal processing using a hybrid deep learning technique

A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology

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