Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks

Tao, Xu; Zhang, Dapeng; Wang, Zihao; Liu, Xiaoyang; Zhang, Hongyan; Xu, De

doi:10.1109/tsmc.2018.2871750

Cited by 471 publications

(238 citation statements)

References 22 publications

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“…In the present research, UAV based image processing for the insulator localization and its surface detection has been carried out using state-of-the-art CNN algorithms such as SSD [24], Yolo9000 [25], Faster RCNN VGG16 [26], Faster RCNN [27] and detection problems [5,28,29].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

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Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator

et al. 2020

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A novel Fusion Convolutional Network (FCN) is proposed in this research for potential realtime monitoring of insulators using unmanned aerial vehicle (UAV) edge devices. Precise airborne imaging of outdoor objects, such as high voltage insulators, suffers from varied object resolution, cluttered backgrounds, unclear or contaminated surfaces, and illumination conditions. Accurate information about the insulator surface condition is essential and is of a high priority since insulator breakdown is a leading cause of electrical failure. A multi-modal information fusion (MMIF) system is developed during this research to analyze and classify possible contaminations present on the electrical insulators. A novel system, referred to as FCN, consists of a Convolutional Neural Network (CNN) and a binary Multilayer Neural Network (MNN) sub-classifier. While constructing the MMIF dataset for training and testing the novel FCN, the image classification output of the CNN is combined with the leakage current values (LCV) obtained as the classification output of MNN. Each sample of the MMIF dataset is, therefore, represented as a series of fusions. Later, sub-classifiers, of the FCN, are trained to identify the contamination types in the fusion series by implementing a voting system of sub-classifiers which is trained to identify a given class. As a result of the implementation of the proposed FCN, the classification accuracy increased by 8.4%, i.e., from 92% to 99.76%. To compare and benchmark the performance of proposed FCN, conventional classification algorithms are also implemented on the fusion of features that are extracted employing the wavelet transform and PCA methods. State-of-the-art CNN architectures are also discussed on account of their time consumption and memory usage. The conceptualization of a potential hardware implementation of the proposed FCN, on emerging edge devices, is also provided for completeness of the discussion. Pertinent outcomes of this research can be further extended to other potential applications of airborne imaging. INDEX TERMS Convolutional Neural Network, multi-modal information fusion, electrical insulators, unmanned aerial vehicle.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

“…Recently, the use of unmanned aerial vehicles (UAVs) has shown potential in airborne imaging for the surveillance of the power lines apart from other applications [5]. The benefits of employing UAVs include, mobility, low operating costs, and the possibility of computer vision with the aid of online edge computing.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator

et al. 2020

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“…Accordingly, it is hard to train an end-to-end network with good performance for insulator faults detection. To address this challenge, Tao et al [51] segment the insulator string that contains insulator fault from an aerial image. Subsequently, they paste the segmented insulator string on another aerial image that only contains background to augment their insulator fault dataset.…”

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 literature [31] introduces an architecture that automatically learns a robust set of feature representations from raw spatio-temporal tomography sensor data. The literature [32] presents a novel deep convolutional neural network cascading architecture for performing localization and detecting defects in insulators. A time-frequency gray scale images are acquired by applying the continuous wavelet transform (CWT) and used for earth fault detection in [33].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Energy Internet Using Correlation Processing-Based Convolutional Neural Networks

Yang

Pang

Zhou

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

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Fault feature extraction based on prior knowledge and raw data is increasingly becoming more challenging in energy internet fault diagnosis due to complicated network topology and coupling disturbances introduced into the systems. Deep learning methods that have emerged in recent years, such as the convolutional neural networks (CNNs), have shown a number of advantages and great potentials in the field of feature extraction and image recognition. However, CNNs does not work well in fault diagnosis for industrial systems, due to the totally different data representations between images used in recognition and signals obtained from industrial processes. This paper tackles this problem by introducing a novel and generic fault diagnosis method for complicated system namely the Spearman rank correlation based CNNs (SR-CNN). By imposing the Spearman rank correlation image layer on the typical CNNs, the multiple time series signals measured by the phasor measurement units (PMUs) is converted to appropriate data images, which are then fed to the CNNs. With the aid of this novel design, different fault features can be comprehensively extracted while the fault can be identified more quickly and precisely than other conventional approaches. To validate the efficacy of the proposed approach, an IEEE defined power gird with multi new energy resources are used as the test platform. The experimental results confirm the effectiveness and superiority of the proposed method in energy internet fault diagnosis over conventional methods.

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Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks

Cited by 471 publications

References 22 publications

Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator

Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator

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

Fault Diagnosis for Energy Internet Using Correlation Processing-Based Convolutional Neural Networks

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