Journal ArticleDOI
Deep-Learning-Based Earth Fault Detection Using Continuous Wavelet Transform and Convolutional Neural Network in Resonant Grounding Distribution Systems
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TLDR
In this paper, a simple and effective method of faulty feeder detection in resonant grounding distribution systems based on the continuous wavelet transform (CWT) and convolutional neural network (CNN) is presented.Abstract:
Feature extraction for fault signals is critical and difficult in all kinds of fault detection schemes. A novel simple and effective method of faulty feeder detection in resonant grounding distribution systems based on the continuous wavelet transform (CWT) and convolutional neural network (CNN) is presented in this paper. The time-frequency gray scale images are acquired by applying the CWT to the collected transient zero-sequence current signals of the faulty feeder and sound feeders. The features of the gray scale image will be extracted adaptively by the CNN, which is trained by a large number of gray scale images under various kinds of fault conditions and factors. The features extraction and the faulty feeder detection can be implemented by the trained CNN simultaneously. As a comparison, two faulty feeder detection methods based on artificial feature extraction and traditional machine learning are introduced. A practical resonant grounding distribution system is simulated in power systems computer aided design/electromagnetic transients including DC, the effectiveness and performance of the proposed faulty feeder detection method is compared and verified under different fault circumstances.read more
Citations
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Journal ArticleDOI
A Review on Deep Learning Applications in Prognostics and Health Management
TL;DR: The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data and suggests the possibility of transfer learning across PHM applications.
Journal ArticleDOI
Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions
TL;DR: A general overview of AI, including its definitions, history and state-of-the-art methodologies, and a comprehensive review of its applications to security assessment, stability assessment, fault diagnosis, and stability control in smart grids are presented.
Journal ArticleDOI
Fault Diagnosis of Rolling Bearings Based on an Improved Stack Autoencoder and Support Vector Machine
TL;DR: Wang et al. as discussed by the authors proposed a feature distance stack autoencoder (FD-SAE) for rolling bearing fault diagnosis, which has stronger feature extraction ability and faster network convergence speed.
Journal ArticleDOI
A Novel HVDC Double-Terminal Non-Synchronous Fault Location Method Based on Convolutional Neural Network
TL;DR: The results show that the proposed fault location method by considering double-end unsynchronized using Hilbert–Huang transform and one-dimensional convolutional neural network (1D-CNN) can reliably and accurately locate line faults under fault resistance up to 5200 Ω.
Journal ArticleDOI
Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network
TL;DR: Wang et al. as mentioned in this paper proposed an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN), which achieved an overall performance of 70.75, 67.47, 68.76, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification
TL;DR: An end-to-end framework for the dense, pixelwise classification of satellite imagery with convolutional neural networks (CNNs) and design a multiscale neuron module that alleviates the common tradeoff between recognition and precise localization is proposed.
Journal ArticleDOI
A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction.
TL;DR: This work proposes an algorithm which uses a deep convolutional neural network which is applied to the wavelet transform coefficients of low‐dose CT images and effectively removes complex noise patterns from CT images derived from a reduced X‐ray dose.
Journal ArticleDOI
High impedance fault detection based on wavelet transform and statistical pattern recognition
TL;DR: In this article, a novel method for high impedance fault (HIF) detection based on pattern recognition systems is presented, using this method, HIFs can be discriminated from insulator leakage current (ILC) and transients such as capacitor switching, load switching (high/low voltage), ground fault, inrush current and no load line switching.
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