Deep Learning Image-Based Defect Detection in High Voltage Electrical Equipment

Ullah, Irfan; Khan, Rehan Ullah; Yang, Fan; Wuttisittikulkij, Lunchakorn

doi:10.3390/en13020392

Cited by 68 publications

(30 citation statements)

References 42 publications

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“…In turn, supervised learning implies an algorithm's ability to recognize elements based on provided samples with the goal of recognizing new data based on training data. Supervised learning algorithms include, for example, decision trees, support vector machines (SVM), naive Bayes classifiers, k-nearest neighbors and linear regressions [7][8][9][10][11][12][13][14][15][16][17][18][19][20][34][35][36][37]. Supervised learning can be further divided into classification and regression: classification means that samples belong to two or more classes, with the goal of predicting the class of unlabeled data from the already-labeled data and thus identifying to which category an object belongs; regression is understood as predicting an attribute associated with an object.…”

Section: Machine Learning and Partial Discharge Image Recognitionmentioning

confidence: 99%

Classification of Partial Discharge Images Using Deep Convolutional Neural Networks

Florkowski

2020

Energies

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Artificial intelligence-based solutions and applications have great potential in various fields of electrical power engineering. The problem of the electrical reliability of power equipment directly refers to the immunity of high-voltage (HV) insulation systems to operating stresses, overvoltages and other stresses—in particular, those involving strong electric fields. Therefore, tracing material degradation processes in insulation systems requires dedicated diagnostics; one of the most reliable quality indicators of high-voltage insulation systems is partial discharge (PD) measurement. In this paper, an example of the application of a neural network to partial discharge images is presented, which is based on the convolutional neural network (CNN) architecture, and used to recognize the stages of the aging of high-voltage electrical insulation based on PD images. Partial discharge images refer to phase-resolved patterns revealing various discharge stages and forms. The test specimens were aged under high electric stress, and the measurement results were saved continuously within a predefined time period. The four distinguishable classes of the electrical insulation degradation process were defined, mimicking the changes that occurred within the electrical insulation in the specimens (i.e., start, middle, end and noise/disturbance), with the goal of properly recognizing these stages in the untrained image samples. The results reflect the exemplary performance of the CNN and its resilience to manipulations of the network architecture and values of the hyperparameters. Convolutional neural networks seem to be a promising component of future autonomous PD expert systems.

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Section: Machine Learning and Partial Discharge Image Recognitionmentioning

confidence: 99%

Classification of Partial Discharge Images Using Deep Convolutional Neural Networks

Florkowski

2020

Energies

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“…In addition, the quantitative evaluation is successively presented according to two aspects: (i) length comparison of the switch arm under different distances and angles and (ii) accuracy comparison of the angle under different distances and angles. The calculation of the arm's length and angle is based on 3D coordinate transformation, which is shown in formula (17). Table 4 presents the length comparison of the switch arm calculated in 3D space by five stereo matching methods and nine different situations with regard to distance and angle.…”

Section: Measurement Analysismentioning

confidence: 99%

“…Further work based on deep learning approach and infrared images has also been carried out to defect analysis in high-voltage equipment. 17 However, the above approaches are all based on twodimensional (2D) image content, in which the angle between the switch arm and the insulator cannot uniquely reflect the true angle in 3D space especially under the changes of the camera angle. Moreover, by using an infrared device to monitor the closing of the switch, it is impossible to accurately determine whether the closure is completely successful at the moment the switch is closed.…”

Section: Introductionmentioning

confidence: 99%

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State measurement of isolating switch using cost fusion and smoothness prior based stereo matching

Luo

et al. 2020

International Journal of Advanced Robotic Systems

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To better monitor the state of isolating switches, an efficient binocular vision-based state measurement system is proposed in this article. Two optimal cameras are selected as the vision of our inspection system. Firstly, stereo calibration and distortion rectification are performed on acquired image pair. Secondly, to recover the three-dimensional information of switch, we propose a semi-global stereo matching method by using data- and structure-driven cost volume fusion and then optimizing raw disparity map with weighted- and edge discriminated-smoothness prior. Gradient content is enforced on the weight for suppressing small-weight-accumulation problem in weak-textured regions. Besides, Hough transform with feature constraints is implemented for removing the chaotic lines and extracting center line of the switch arm. Finally, based on the center line and corresponding disparity map of the switch arm, triangulation principle is used for calculating the true angle between the switch arm and insulator such that whether or not the isolating switch is fully closed can be detected. The experimental results demonstrate that the proposed stereo matching method can achieve good performance in Middlebury v.3 data set and switch images, and the system can precisely measure the state of switches.

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“…The application of machine learning, especially neural networks in this field, was observed in the early 1990s, e.g., [3][4][5][6][7][8][9][10][11][12][13][14], which is when the first automated analyzers and expert systems with AI elements were designed, e.g., [7][8][9]13,[15][16][17][18][19]. Therefore, one can observe a continuous strive for advancements in evaluation methods, instrumentation, methodologies, and algorithms of future diagnostics and monitoring systems of HV insulation of power equipment [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. Over the last decade, strong attention in pattern recognition was paid on deep neural networks as a technique presenting high performance, e.g., [22,[25][26][27][33]…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns

Florkowski

2021

Energies

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In the resilient and reliable electrical power system, the condition of high voltage insulation plays a crucial role. In the field of high voltage insulation integrity, the partial discharge (PD) inception and development trends are essential for assessment criteria in diagnostics systems. The observed trend to employ more and more sophisticated algorithms with machine learning features and artificial intelligence (AI) elements is observed everywhere. The classification and identification of features in PD images is perceived as a critical requirement for an effective high voltage insulation diagnosis. In this context, techniques allowing for anomaly detection, trends observation, and feature extraction in partial discharge patterns are important. In this paper, the application of few algorithms belonging to image processing, machine learning and optical flow is presented. The feature extraction refers to image segmentation and detection of coherent forms in the images. The anomaly detection algorithms can trigger early detection of the trend changes or the appearance of a new discharge form, and hence are suitable for PD monitoring applications. Anomaly detection can also handle transients and disturbances that appear in the PD image as an indication of an abnormal state. The future monitoring systems should be equipped with trend evolution algorithms. In this context, two examples of insulation aging and application of PD-based monitoring are shown. The first one refers to deep convolutional neural networks used for classification of deterioration stages in high voltage insulation. The latter one demonstrates application of optical flow approach for motion detection in partial discharge images. The motivation for the research was the strive to machine-controlled pattern analysis, leading towards intelligent PD-based diagnostics.

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Deep Learning Image-Based Defect Detection in High Voltage Electrical Equipment

Cited by 68 publications

References 42 publications

Classification of Partial Discharge Images Using Deep Convolutional Neural Networks

Classification of Partial Discharge Images Using Deep Convolutional Neural Networks

State measurement of isolating switch using cost fusion and smoothness prior based stereo matching

Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns

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