Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress

Barrios, Sonia; Buldain, David; Comech, M.P.; Gilbert, Ian; Orúe, I.

doi:10.3390/en12132485

Cited by 88 publications

(47 citation statements)

References 27 publications

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“…The diversity of PD sources to be treated makes the diagnosis even more difficult when dealing with real online measurement. As reported in [43], [47], [48], most of PD source recognition techniques developed in the literature were mainly trained on artificial defect models in laboratory, which may not function well when put into practice in PD online measurement scenarios. In our case, we tested our method on more than unlabeled PD measurement files collected by Hydro-Québec during the last 30 years.…”

Section: A Expert Knowledge For Transfer Learningmentioning

confidence: 99%

Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators

et al. 2020

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Hydrogenerators are strategic assets for power utilities. Their reliability and availability can lead to significant benefits. For decades, monitoring and diagnosis of hydrogenerators have been at the core of maintenance strategies. A significant part of generator diagnosis relies on Partial Discharge (PD) measurements, because the main cause of hydrogenerator breakdown comes from failure of its high voltage stator, which is a major component of hydrogenerators. A study of all stator failure mechanisms reveals that more than 85 % of them involve the presence of PD activity. PD signal can be detected from the lead of the hydrogenerator while it is running, thus allowing for on-line diagnosis. Hydro-Québec has been collecting more than 33 000 unlabeled PD measurement files over the last decades. Up to now, this diagnostic technique has been quantified based on global PD amplitudes and integrated PD energy irrespective of the source of the PD signal. Several PD sources exist and they all have different relative risk, but in order to recognize the nature of the PD, or its source, the judgement of experts is required. In this paper, we propose a new method based on visual data analysis to build a PD source classifier with a minimum of labeled data. A convolutional variational autoencoder has been used to help experts to visually select the best training data set in order to improve the performances of the PD source classifier.

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Section: A Expert Knowledge For Transfer Learningmentioning

confidence: 99%

Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators

et al. 2020

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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%

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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“…Due to the large amount of information contained in the constructed PD feature expression, it is subjective to extract one or several features of PD signal through complex artificial design, and the traditional shallow-layer recognition method has poor processing ability for high-dimensional data, which easily leads to the lack of generalization ability of the model, so it is hard to achieve good recognition effect. In recent years, deep learning has been widely used due to its advantages of automatic feature extraction and classification, which brings new opportunities for pattern recognition of PD [11]. In [12], Karimi et al have used deep belief network (DBN) to identify three types of PD and achieved good results.…”

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 Discharge Classification Using Deep Learning Methods—Survey of Recent Progress

Cited by 88 publications

References 27 publications

Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators

Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators

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

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