Pollution Severity Monitoring of High Voltage Transmission Line Insulators Using Wireless Device Based on Leakage Current Bursts

Self Cite

This paper reviews the applications of Internet of Things (IoT) and digital twin technology in electrical power systems. It begins by discussing the generalized IoT value chain, followed by the terminology of smart grid, with clarifying the role of IoT‐systems and the digital twin structure within the Smart Grid. A comparison between different short‐range and long‐range transports is presented. The paper then discusses the use of IoT and digital twin technology for effective energy management with applications in smart homes, smart buildings, smart grids, smart industries (Industry 4.0), smart transportation and smart cities. Additionally, the paper explores the use of IoT and digital twin technology for condition monitoring and diagnosis (CMD) in electrical power systems. Three different cases are presented for CMD, that is, CMD of power transformers, CMD of electrical grids and CMD of substations. IoT and digital twin applications are also highlighted in power electronic systems. Finally, the paper discusses the challenges and opportunities of applying IoT and digital twin technology to electrical power systems and provides recommendations for future research.

Section: Iot and Digital Twin For Condition Monitoring And Diagnosismentioning

confidence: 99%

Applications of IoT and digital twin in electrical power systems: A comprehensive survey

Mansour

Numair

Zalhaf

et al. 2023

Self Cite

“…These potential risks would lead to transmission interruption and threaten the security of the power system. Related research [1,2,52] indicates that 81.3% of power accidents in power transmission systems are caused by insulator defects. Additionally, researchers [49,50,53] find that the cyber-attacks and insulation defects classification also play an important role in online monitoring of the Internet of Things architecture in smart grid recently.…”

Section: Introductionmentioning

confidence: 99%

Insulator defect detection with deep learning: A survey

Liu

Huang

et al. 2023

With the improvement of smart grid, utilizing unmanned aerial vehicles (UAV) to detect the operation status of insulators has attracted widespread attention. The insulator defects can lead to serious power loss, damage the service life of power lines, and even result in power outages in serious cases. The small‐scale object, complex background, and limited‐number collected data make insulator defect still a challenging problem. Benefitted by the advances in deep learning, deep learning‐based insulator defects have achieved great progress in recent years. In the paper, the authors present a novel systematic survey of these advances, where further analysis about different processing stages methods is introduced: (i) insulator processing stage methods exploit the specific image pre‐processing algorithm for data augmentation and low‐level vision information extraction; (ii) defect detection stage model can locate and classify diagnosis fault with different task targets, like sequential task strategy and multi‐task strategy. In addition, the authors also review publicly available benchmark and datasets. The future research direction and open problem are discussed to promote the development of the community.

“…However, manual inspection is time-consuming and resourceintensive. With the widespread application of the Electric Internet of Things, the accuracy and anti-interference capability of online monitoring devices for electrical equipment have been greatly enhanced, allowing for the image and video surveillance monitoring of wildfires [7][8][9]. However, due to the limited number of devices, image and video surveillance can only detect a small number of wildfires [10].…”

Section: Introductionmentioning

confidence: 99%

Monitoring and alarm method for wildfires near transmission lines with multi‐Doppler weather radars

Shu

Chen

Cao

et al. 2023

Monitoring and alarm play an important role in preventing trip-out caused by wildfires for high-voltage transmission lines. This paper proposes a multi-Doppler weather radar-based method for monitoring and alerting of wildfires near transmission lines. Firstly, characteristic parameters are mathematically proposed to distinguish wildfire's radar echoes. Then, the four-site neighbourhood algorithm and the multithreading method are used to extract all possible wildfire echo units detected by various Doppler weather radars, and a cumulative probability is calculated to identify the wildfire. Finally, a regional block search method is used to quickly locate wildfire-affected transmission towers. The membership function of spread time in the fuzzy set is proposed to determine the alarm level, which takes into account the influences of environment, topography, and vegetation on wildfire spread rate. The application to a provincial power grid demonstrates that the proposed method has an accuracy of 82.4%, a missing alarm rate of 27.4%, and a delay of fewer than 15 min. In addition, the joint observation of wildfires by multi-Doppler weather radars and satellites indicates a promising application prospect for transmission line wildfire fighting.