Journal ArticleDOI
Deep Learning Based Module Defect Analysis for Large-Scale Photovoltaic Farms
TLDR
This paper presents a deep learning based solution for defect pattern recognition by the use of aerial images obtained from unmanned aerial vehicles that significantly improves the efficiency and accuracy of asset inspection and health assessment for large-scale PV farms in comparison with the conventional solutions.Abstract:
The efficient condition monitoring and accurate module defect detection in large-scale photovoltaic (PV) farms demand for novel inspection method and analysis tools. This paper presents a deep learning based solution for defect pattern recognition by the use of aerial images obtained from unmanned aerial vehicles. The convolutional neural network is used in the machine learning process to classify various forms of module defects. Such a supervised learning process can extract a range of deep features of operating PV modules. It significantly improves the efficiency and accuracy of asset inspection and health assessment for large-scale PV farms in comparison with the conventional solutions. The proposed algorithmic solution is extensively evaluated from different aspects, and the numerical result clearly demonstrates its effectiveness for efficient defect detection of PV modules.read more
Citations
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Journal ArticleDOI
Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions
TL;DR: A novel intelligent fault detection and diagnosis method for photovoltaic arrays based on a newly designed deep residual network model trained by the adaptive moment estimation deep learning algorithm, which can automatically extract features from raw current-voltage curves and ambient irradiance and temperature, and effectively improve the performance with a deeper network.
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Machine learning driven smart electric power systems: Current trends and new perspectives
TL;DR: This study demonstrates the increasing interest and rapid expansion in the use of machine learning techniques to successfully address the technical challenges of the smart grid from various aspects and provides a preliminary foundation for further exploration and development of related knowledge and insights.
Journal ArticleDOI
CNN based automatic detection of photovoltaic cell defects in electroluminescence images
M. Waqar Akram,M. Waqar Akram,Guiqiang Li,Yi Jin,Xiao Chen,Changan Zhu,Xudong Zhao,Abdul Khaliq,Muhammad Faheem,Ashfaq Ahmad,Ashfaq Ahmad +10 more
TL;DR: A novel approach using light convolutional neural network architecture for recognizing defects in EL images which achieves state of the art results of 93.02% on solar cell dataset of EL images.
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
Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review
TL;DR: A systematic study on the application of ANN and hybridized ANN models for PV fault detection and diagnosis (FDD) is conducted and the main trends, challenges and prospects are presented.
References
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin,Holger R. Roth,Mingchen Gao,Le Lu,Ziyue Xu,Isabella Nogues,Jianhua Yao,Daniel J. Mollura,Ronald M. Summers +8 more
TL;DR: Two specific computer-aided detection problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification are studied, achieving the state-of-the-art performance on the mediastinal LN detection, and the first five-fold cross-validation classification results are reported.
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Large-margin softmax loss for convolutional neural networks
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Large-Margin Softmax Loss for Convolutional Neural Networks
TL;DR: In this article, a generalized large-margin softmax (L-Softmax) loss is proposed to encourage intra-class compactness and inter-class separability between learned features.
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