Recognition of pollution layer location in 11 kV polymer insulators used in smart power grid using dual-input VGG Convolutional Neural Network

Vigneshwaran, B.; Maheswari, R. V.; Kalaivani, L.; Shanmuganathan, Vimal; Rho, Seungmin; Kadry, Seifedine; Lee, Mi Young

doi:10.1016/j.egyr.2020.12.044

Cited by 21 publications

(3 citation statements)

References 41 publications

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“…VGG is a CNN architecture developed by Karen Simonyan and Andrew Zisserman in 2014, which achieved state-of-the-art performance on the ImageNet dataset [133][134][135][136][137]. The VGG architecture consists of multiple layers of small 3 × 3 convolutional filters, followed by max Pool_Lays.…”

Section: Vggmentioning

confidence: 99%

Convolutional Neural Networks: A Survey

Krichen

2023

Computers

197

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Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development.

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Section: Vggmentioning

confidence: 99%

Convolutional Neural Networks: A Survey

Krichen

2023

Computers

197

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“…In general, the literature shows an inadequate amount of work that has been devoted to train deep network architectures to classify and predict pollution levels non-intrusively. All the work done is either focused on applying deep learning models to intrusive measurement techniques [130,131] or applying classical machine learning using non-intrusive techniques [132][133][134][135]. More research is needed to combine deep learning models with non-intrusive approaches for monitoring, particularly those based on radiation type sensors.…”

Section: Contamination Diagnosismentioning

confidence: 99%

Deep Learning in High Voltage Engineering: A Literature Review

et al. 2022

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Condition monitoring of high voltage apparatus is of much importance for the maintenance of electric power systems. Whether it is detecting faults or partial discharges that take place in high voltage equipment, or detecting contamination and degradation of outdoor insulators, deep learning which is a branch of machine learning has been extensively investigated. Instead of using hand-crafted manual features as an input for the traditional machine learning algorithms, deep learning algorithms use raw data as the input where the feature extraction stage is integrated in the learning stage, resulting in a more automated process. This is the main advantage of using deep learning instead of traditional machine learning techniques. This paper presents a review of the recent literature on the application of deep learning techniques in monitoring high voltage apparatus such as GIS, transformers, cables, rotating machines, and outdoor insulators.

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“…VGGs are also used in other areas, such as smart grids and breast cancer detection. The use of a partial discharge (PD) signal paired with a dual-input VGG-based CNN to forecast the position of the pollution layer on 11 kV polymer insulators subjected to alternating current for smart grid systems was investigated by Vigneshwaran et al [ 37 ]. GSB Jahangeer and Dhiliphan Rajkumar [ 38 ] sought to discover the early detection of breast cancer through a combination of a series of network and VGG16-based models.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Identification of Jute Pests Based on Transfer Learning and Deep Convolutional Neural Networks

Sourav

Wang

2022

Neural Process Lett

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Pest attacks pose a substantial threat to jute production and other significant crop plants. Jute farmers in Bangladesh generally distinguish between different pests that appear to be the same using their eyes and expertise, which isn't always accurate. We developed an intelligent model for jute pests identification based on transfer learning (TL) and deep convolutional neural networks (DCNN) to solve this practical problem. The proposed DCNN model can realize fast and accurate automatic identification of jute pests based on photographs. Specifically, the VGG19 CNN model was trained by TL on the ImageNet database. A well-structured image dataset of four dominant jute pests is also established. Our model shows a final accuracy of 95.86% on the four most vital jute pest classes. The model’s performance is further demonstrated by the precision, recall, F1-score, and confusion matrix results. The proposed model is integrated into Android and IOS applications for practical uses.

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Recognition of pollution layer location in 11 kV polymer insulators used in smart power grid using dual-input VGG Convolutional Neural Network

Cited by 21 publications

References 41 publications

Convolutional Neural Networks: A Survey

Convolutional Neural Networks: A Survey

Deep Learning in High Voltage Engineering: A Literature Review

Intelligent Identification of Jute Pests Based on Transfer Learning and Deep Convolutional Neural Networks

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