A Convolutional Neural Network-Based Deep Learning Methodology for Recognition of Partial Discharge Patterns from High-Voltage Cables

Peng, Xiaosheng; Fan, Yilin; Wang, Ganjun; Wu, Yao; Li, Lee; Li, Zhaohui; Bhatti, Ashfaque Ahmed; Zhou, Chengke; Hepburn, Donald M.; Reid, Alistair; Judd, M.D.; Siew, W.H.

doi:10.1109/tpwrd.2019.2906086

Cited by 126 publications

(43 citation statements)

References 23 publications

(60 reference statements)

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“…Different 2D convolution topologies were tested (usually with 64-128 filter channels); each stage was followed by the MaxPooling layer. Applying a lower number of filters (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) resulted in the rapid downgrading of the accuracy by 30%. Two types of kernel sizes were compared: 3 × 3 and 5 × 5 (input image size of 128 × 128 pixels; stride was equal to 2).…”

Section: Discussionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

“…This signal is passed through an activation function in the following step. In most cases, the rectified linear unit (ReLU) transform function is applied [1,29,32]:…”

Section: Architecture Of Deep Convolutional Neural Networkmentioning

confidence: 99%

Classification of Partial Discharge Images Using Deep Convolutional Neural Networks

Florkowski

2020

Energies

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“…Before using network to identify PD signal types, the basic structure of the network should be determined according to the characteristics of the sample set. Generally, with the increase of network depth, the feature extraction ability of CNN is gradually enhanced, but the number of network parameters to be trained is also increased, and the risk of overfitting is easy to occur for the sample set with insufficient data [29].…”

Section: ) Construction Of Basic Network Structurementioning

confidence: 99%

“…In addition, the size of convolution kernel is of significant importance to the PD pattern recognition performance of CNN [29]. On the basis of the network structure consisting of 3 convolution layers and corresponding pooling layers, the size of the convolution kernel is set to 3×3, 5×5, 7×7, 9×9, respectively.…”

Section: ) Construction Of Basic Network Structurementioning

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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“…The fully connected layer unfolds the features extracted through the multi-layer convolution layer and the pooling layer in one dimension, and then maps the results to the output layer according to (5). Then the output layer outputs the classification results through a classifier [19].…”

Section: A Structure Of Br-cnnmentioning

confidence: 99%

An Auxiliary Fault Identification Strategy of Flexible HVDC Grid Based on Convolutional Neural Network With Branch Structures

Mashino

Liu²,

Zhan³

et al. 2020

IEEE Access

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Lightning disturbance may be misjudged as dc fault by the primary protection in the flexible high voltage dc (HVDC) grid. To solve this problem, an auxiliary fault identification strategy based on convolutional neural network with branch structures (BR-CNN) is proposed in this paper. In the proposed scheme, the voltage and current characteristic matrix is constructed as the input matrix of BR-CNN model and the output categories include positive pole-to-ground (PTG) fault and lightning disturbance. Voltage and current branches are constructed to extract high-level local features of input data layer by layer, and main branch is designed to realize the comprehensive utilization of voltage and current information. Through autonomous learning of the model, the nonlinear mapping relationship between input and output is constructed. The method only uses the single-terminal quantities, and can be used as an auxiliary criterion to improve the reliability of the primary protection. The test results verify the effectiveness of the method, and the recognition accuracy is better than the traditional classification models.

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A Convolutional Neural Network-Based Deep Learning Methodology for Recognition of Partial Discharge Patterns from High-Voltage Cables

Cited by 126 publications

References 23 publications

Classification of Partial Discharge Images Using Deep Convolutional Neural Networks

Classification of Partial Discharge Images Using Deep Convolutional Neural Networks

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

An Auxiliary Fault Identification Strategy of Flexible HVDC Grid Based on Convolutional Neural Network With Branch Structures

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