Kunjabihari Swain¹, Murthy Cherukuri^1,*, Indu Sekhar Samanta², Bhargav Appasani^3,*, Nicu Bizon^4,5, Mihai Oproescu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1993-2015, 2025, DOI:10.32604/cmes.2025.067121 - 26 November 2025

Abstract Transmission line faults pose a significant threat to power system resilience, underscoring the need for accurate and rapid fault identification to facilitate proper resource monitoring, economic loss prevention, and blackout avoidance. Extreme learning machine (ELM) offers a compelling solution for rapid classification, achieving network training in a single epoch. Leveraging the Internet of Things (IoT) and the virtual instrumentation capabilities of LabVIEW, ELM can enable the swift and precise identification of transmission line faults. This paper presents a regularized radial basis function (RBF) ELM-based fault detection and classification system for transmission lines, utilizing a LabVIEW More >

Long Zhao^1,*, Qi Zhao¹, Siyuan Zhou¹, Chenyang Fan², Chao Ji¹

Frontiers in Heat and Mass Transfer, Vol.23, No.5, pp. 1455-1475, 2025, DOI:10.32604/fhmt.2025.069512 - 31 October 2025

Abstract The temperature prediction of the Clamp-conductor coupling zone plays a crucial role in ensuring the safe and stable operation of overhead transmission lines and optimizing the thermal stability margin of transmission lines. While existing research in this field has thoroughly explored temperature rise prediction, the focus has been relatively narrow, either targeting conductors exclusively or focusing solely on clamps, with little attention given to the temperature rise in the conductor-clamp coupling zone or the influence of clamp temperature on conductor temperature rise. Based on this, considering axial heat transfer between the clamp and the conductor,… More >

Hao Wang^*, Wenyue Zhou, Yiping Luo

Energy Engineering, Vol.122, No.9, pp. 3737-3752, 2025, DOI:10.32604/ee.2025.067938 - 26 August 2025

Abstract Currently, renewable energy has been broadly implemented across diverse sectors, particularly evidenced by its substantially higher integration levels in power systems. The large-scale integration of renewable energy resources introduces distinct fault characteristics into power grids, potentially rendering traditional line protection schemes inadequate for transmission lines interfacing with these sources. Therefore, it is imperative to study line protection methods unaffected by the integration of renewable energy resources. After analyzing the propagation process of the fault traveling wave along the transmission line, a non-unit protection based on traveling wave distance measurement is proposed. The core principle of… More >

Mingguan Zhao^1,2,*, Xinsheng Dong^1,2, Yang Yang^1,2, Meng Li^1,2, Hongxia Wang^1,2, Shuyang Ma^1,2, Rui Zhu³, Xiaojing Zhu³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 2091-2109, 2025, DOI:10.32604/cmes.2025.059169 - 27 January 2025

Abstract Ice cover on transmission lines is a significant issue that affects the safe operation of the power system. Accurate calculation of the thickness of wire icing can effectively prevent economic losses caused by ice disasters and reduce the impact of power outages on residents. However, under extreme weather conditions, strong instantaneous wind can cause tension sensors to fail, resulting in significant errors in the calculation of icing thickness in traditional mechanics-based models. In this paper, we propose a dynamic prediction model of wire icing thickness that can adapt to extreme weather environments. The model expands… More >

Xunguang Yan^1,2, Wenrui Wang¹, Fanglin Lu¹, Hongyong Fan³, Bo Wu¹, Jianfeng Yu^1,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1439-1458, 2025, DOI:10.32604/cmc.2024.057797 - 03 January 2025

Abstract To maintain the reliability of power systems, routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues. The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods, especially in identifying small objects in high-resolution images. This study presents an enhanced object detection algorithm based on the Faster Region-based Convolutional Neural Network (Faster R-CNN) framework, specifically tailored for detecting small-scale electrical components like insulators, shock hammers, and screws in transmission line. The algorithm features an improved backbone network for Faster R-CNN, which significantly boosts the More >

Zhe Li, Yun Liang, Jinyu Wang, Yang Gao^*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1171-1192, 2025, DOI:10.32604/cmc.2024.057225 - 03 January 2025

Abstract Iced transmission line galloping poses a significant threat to the safety and reliability of power systems, leading directly to line tripping, disconnections, and power outages. Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source, neglect of irregular time series, and lack of attention-based closed-loop feedback, resulting in high rates of missed and false alarms. To address these challenges, we propose an Internet of Things (IoT) empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather… More >

Yinkong Wei^1,2, Mucong Wu^1,2,*, Wei Wei³, Paulo R. F. Rocha⁴, Ziyi Cheng^1,2, Weifang Yao⁵

Computer Systems Science and Engineering, Vol.48, No.3, pp. 723-738, 2024, DOI:10.32604/csse.2023.036062 - 20 May 2024

Abstract Ultra-high voltage (UHV) transmission lines are an important part of China’s power grid and are often surrounded by a complex electromagnetic environment. The ground total electric field is considered a main electromagnetic environment indicator of UHV transmission lines and is currently employed for reliable long-term operation of the power grid. Yet, the accurate prediction of the ground total electric field remains a technical challenge. In this work, we collected the total electric field data from the Ningdong-Zhejiang ±800 kV UHVDC transmission project, as of the Ling Shao line, and perform an outlier analysis of the More >

Yakui Liu^1,2,3,*, Xing Jiang¹, Ruikang Xu¹, Yihao Cui¹, Chenhui Yu¹, Jingqi Yang¹, Jishuai Zhou¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1263-1279, 2024, DOI:10.32604/cmc.2024.048864 - 25 April 2024

Abstract The rapid pace of urban development has resulted in the widespread presence of construction equipment and increasingly complex conditions in transmission corridors. These conditions pose a serious threat to the safe operation of the power grid. Machine vision technology, particularly object recognition technology, has been widely employed to identify foreign objects in transmission line images. Despite its wide application, the technique faces limitations due to the complex environmental background and other auxiliary factors. To address these challenges, this study introduces an improved YOLOv8n. The traditional stepwise convolution and pooling layers are replaced with a spatial-depth… More >

Guangxin Zhang¹, Minghui Liu², Shichao Cheng³, Minzhen Wang^1,*, Changshun Zhao⁴, Hongdan Zhao⁵, Gaiming Zhong¹

Energy Engineering, Vol.121, No.1, pp. 169-185, 2024, DOI:10.32604/ee.2023.027907 - 27 December 2023

Abstract The transmission line tower will be affected by bad weather and artificial subsidence caused by the foundation and other factors in the power transmission. The tower’s tilt and severe deformation will cause the building to collapse. Many small changes caused the tower’s collapse, but the early staff often could not intuitively notice the changes in the tower’s state. In the current tower online monitoring system, terminal equipment often needs to replace batteries frequently due to premature exhaustion of power. According to the need for real-time measurement of power line tower, this research designed a real-time… More >

Xinliang Tang¹, Xiaotong Ru¹, Jingfang Su^1,*, Gabriel Adonis²

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2997-3011, 2023, DOI:10.32604/cmc.2023.038923 - 08 October 2023

Abstract On the transmission line, the invasion of foreign objects such as kites, plastic bags, and balloons and the damage to electronic components are common transmission line faults. Detecting these faults is of great significance for the safe operation of power systems. Therefore, a YOLOv5 target detection method based on a deep convolution neural network is proposed. In this paper, Mobilenetv2 is used to replace Cross Stage Partial (CSP)-Darknet53 as the backbone. The structure uses depth-wise separable convolution toreduce the amount of calculation and parameters; improve the detection rate. At the same time, to compensate for… More >