Partial Discharges Pattern Recognition of Transformer Defect Model by LBP &amp; HOG Features

Firuzi, Keyvan; Vakilian, Mehdi; Phung, B.T.; Blackburn, T.R.

doi:10.1109/tpwrd.2018.2872820

Cited by 98 publications

(41 citation statements)

References 35 publications

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“…In order to represent the characteristic information of PD more effectively, this study uses the two-dimensional PRPD pattern to describe the phase information of PD. The PRPD pattern is represented by a ϕ-u two-dimensional pattern, where ϕ represents the power frequency phase at which PD occurs and u represents the intensity of PD signals [13]. The color of the PRPD pattern represents the discharge density at certain phase and amplitude, which is shown by the color bar.…”

Section: Analysis Of the Experimental Resultsmentioning

confidence: 99%

A Novel Partial Discharge Detection Method Based on the Photoelectric Fusion Pattern in GIL

Zang

Qian

Liu³

et al. 2019

Energies

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Optical detection and ultrahigh frequency (UHF) detection are two significant methods of partial discharge (PD) detection in the gas-insulated transmission lines (GIL), however, there is a phenomenon of signals loss when using two types of detections to monitor PD signals of different defects, such as needle defect and free particle defect. This makes the optical and UHF signals not correspond strictly to the actual PD signals, and therefore the characteristic information of optical PD patterns and UHF PD patterns is incomplete which reduces the accuracy of the pattern recognition. Therefore, an image fusion algorithm based on improved non-subsampled contourlet transform (NSCT) is proposed in this study. The optical pattern is fused with the UHF pattern to achieve the complementarity of the two detection methods, avoiding the PD signals loss of different defects. By constructing the experimental platform of optical-UHF integrated detection for GIL, phase-resolved partial discharge (PRPD) patterns of three defects were obtained. After that, the image fusion algorithm based on the local entropy and the phase congruency was used to produce the photoelectric fusion PD pattern. Before the pattern recognition, 28 characteristic parameters are extracted from the photoelectric fusion pattern, and then the dimension of the feature space is reduced to eight by the principal component analysis. Finally, three kinds of classifiers, including the linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN), are used for the pattern recognition. The results show that the recognition rate of all the photoelectric fusion pattern under different classifiers is higher than that of optical and UHF patterns, up to the maximum of 95%. Moreover, the photoelectric fusion pattern not only greatly improves the recognition rate of the needle defect and the free particle defect, but the recognition accuracy of the floating defect is also slightly improved.

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Section: Analysis Of the Experimental Resultsmentioning

confidence: 99%

A Novel Partial Discharge Detection Method Based on the Photoelectric Fusion Pattern in GIL

Zang

Qian

Liu³

et al. 2019

Energies

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“…These multi-view DS are fed into HOG to extract orientation features [ 72 ]. It calculates magnitude and gradient by dividing the image into 8 × 8 cells which are stored in a 9-bin histogram.…”

Section: Proposed System Methodologymentioning

confidence: 99%

Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

Jalal

Khalid

Kim

2020

Entropy

101

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Automatic identification of human interaction is a challenging task especially in dynamic environments with cluttered backgrounds from video sequences. Advancements in computer vision sensor technologies provide powerful effects in human interaction recognition (HIR) during routine daily life. In this paper, we propose a novel features extraction method which incorporates robust entropy optimization and an efficient Maximum Entropy Markov Model (MEMM) for HIR via multiple vision sensors. The main objectives of proposed methodology are: (1) to propose a hybrid of four novel features—i.e., spatio-temporal features, energy-based features, shape based angular and geometric features—and a motion-orthogonal histogram of oriented gradient (MO-HOG); (2) to encode hybrid feature descriptors using a codebook, a Gaussian mixture model (GMM) and fisher encoding; (3) to optimize the encoded feature using a cross entropy optimization function; (4) to apply a MEMM classification algorithm to examine empirical expectations and highest entropy, which measure pattern variances to achieve outperformed HIR accuracy results. Our system is tested over three well-known datasets: SBU Kinect interaction; UoL 3D social activity; UT-interaction datasets. Through wide experimentations, the proposed features extraction algorithm, along with cross entropy optimization, has achieved the average accuracy rate of 91.25% with SBU, 90.4% with UoL and 87.4% with UT-Interaction datasets. The proposed HIR system will be applicable to a wide variety of man–machine interfaces, such as public-place surveillance, future medical applications, virtual reality, fitness exercises and 3D interactive gaming.

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“…Even in the case of a small number of samples, CFFO-CNN can still achieve a high accuracy of more than 80%, which demonstrates that deep learning network has more superior feature extraction capability that enables CFFO-CNN to fully analyze the time-frequency characteristics of the input image, extract the deeper features superior to the general statistical parameters, and obtain a better classification effect. To verify the effectiveness of the features extracted by CFFO-CNN, the PD pattern recognition tests are carried out in classifier BPNN, meanwhile, three traditional image feature extraction methods including histogram of oriented gradient (HOG) [1], local binary pattern (LBP) [1] and graylevel co-occurrence matrix (GLCM) [30] are compared with the proposed method. The recognition results are shown in Table 5.…”

Section: Comparison With Traditional Recognition Methodsmentioning

confidence: 99%

“…However, in the production, transportation, installation and long-term operation of transformers, various insulation defects will inevitably appear. Among them, partial discharge (PD) is the main reason for the final breakdown of insulation of transformers, and it is also an important manifestation of the internal insulation degradation [1]. Due to the difference of insulation degradation mechanism among different discharge types, the degree of damage to the equipment is also distinct.…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition of Partial Discharge Based on VMD-CWD Spectrum and Optimized CNN With Cross-Layer Feature Fusion

et al. 2020

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In order to improve the recognition accuracy of partial discharge (PD) by making full use of the time-frequency characteristics of PD signals and employing deep learning theory, a kind of PD pattern recognition method based on variational mode decompositon (VMD)-Choi-Williams distribution (CWD) spectrum and optimized convolutional neural network (CNN) with cross-layer feature fusion is proposed in this paper. Firstly, a PD signal is decomposed into several components by VMD algorithm, and the CWD analysis of the obtained components is carried out to obtain the VMD-CWD time-frequency spectrum. Secondly, the cross-layer feature fusion and optimization CNN (CFFO-CNN) is constructed by introducing cross-layer connection and optimization algorithm. Thirdly, the VMD-CWD is regarded as the input vector to train CFFO-CNN to learn and extract the intrinsic features of the spectrum. Finally, the trained network is used to recognize the PD types of the test samples. The proposed method is compared with traditional recognition methods such as BP neural network (BPNN) and support vector machine (SVM), as well as some commonly used deep learning algorithms. The experimental results indicate that the recognition performance of the proposed method is significantly better than that of existing recognition methods with accuracy up to 99.5%. It is proved that CFFO-CNN has superior feature extraction ability, which can extract the internal features of the VMD-CWD spectrum independently with higher recognition accuracy and wider application prospect. INDEX TERMS Variational mode decomposition (VMD), Choi-Williams distribution (CWD), feature fusion, convolutional neural network (CNN), partial discharge, pattern recognition.

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Partial Discharges Pattern Recognition of Transformer Defect Model by LBP & HOG Features

Cited by 98 publications

References 35 publications

A Novel Partial Discharge Detection Method Based on the Photoelectric Fusion Pattern in GIL

A Novel Partial Discharge Detection Method Based on the Photoelectric Fusion Pattern in GIL

Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors

Pattern Recognition of Partial Discharge Based on VMD-CWD Spectrum and Optimized CNN With Cross-Layer Feature Fusion

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