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Review of Deep Learning-Based Intelligent Inspection Research for Transmission Lines 认领 引用
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作者 Jingjing Liu Chuanyang Liu 《Computers, Materials & Continua》 SCIE EI 2026年第5期155-198,共44页
Intelligent inspection of transmission lines enables efficient automated fault detection by integrating artificial intelligence,robotics,and other related technologies.It plays a key role in ensuring power grid safety... Intelligent inspection of transmission lines enables efficient automated fault detection by integrating artificial intelligence,robotics,and other related technologies.It plays a key role in ensuring power grid safety,reducing operation and maintenance costs,driving the digital transformation of the power industry,and facilitating the achievement of the dual-carbon goals.This review focuses on vision-based power line inspection,with deep learning as the core perspective to systematically analyze the latest research advancements in this field.Firstly,at the technical foundation level,it elaborates on deep learning algorithms for intelligent transmission line inspection based on image perception,covering object detection algorithms,semantic segmentation algorithms,and other relevant methodologies.Secondly,in application practice,it summarizes deep learning-based intelligent inspection applications across six dimensions—including detection of power insulators and their defects,transmission tower detection,power line feature extraction,metal fitting and defect detection,thermal fault diagnosis of power components,and safety hazard detection in power scenarios,and further lists relevant public datasets.Finally,in response to current challenges,it identifies five key future research directions,such as the deep integration of multiple learning paradigms,multi-modal data fusion,collaborative application of large and small models,cloud-edge-end collaborative integration,and multi-agent cluster control.This paper reviews and analyzes numerous deep learning-based intelligent detectionmethods for aerial images,comprehensively explores the application of deep learning in Unmanned Aerial Vehicle(UAV)inspection scenarios,and thus provides valuable theoretical and practical references for scholars engaged in smart grid automated inspection research. 展开更多
关键词 Intelligent inspection transmission lines deep learning defect detection
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Line Fault Detection of DC Distribution Networks Using the Artificial Neural Network 认领 引用
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作者 Xunyou Zhang Chuanyang Liu Zuo Sun 《Energy Engineering》 EI 2023年第7期1667-1683,共17页
ADC distribution network is an effective solution for increasing renewable energy utilization with distinct benefits,such as high efficiency and easy control.However,a sudden increase in the current after the occurren... ADC distribution network is an effective solution for increasing renewable energy utilization with distinct benefits,such as high efficiency and easy control.However,a sudden increase in the current after the occurrence of faults in the network may adversely affect network stability.This study proposes an artificial neural network(ANN)-based fault detection and protection method for DC distribution networks.The ANN is applied to a classifier for different faults ontheDC line.The backpropagationneuralnetwork is used to predict the line current,and the fault detection threshold is obtained on the basis of the difference between the predicted current and the actual current.The proposed method only uses local signals,with no requirement of a strict communication link.Simulation experiments are conducted for the proposed algorithm on a two-terminal DC distribution network modeled in the PSCAD/EMTDC and developed on the MATLAB platform.The results confirm that the proposed method can accurately detect and classify line faults within a few milliseconds and is not affected by fault locations,fault resistance,noise,and communication delay. 展开更多
关键词 Artificial neural network DC distribution network fault detection
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