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![Population growth versus energy and electricity demand [4].](publication/342434540/figure/fig2/AS:906123251023872@1593048026306/Population-growth-versus-energy-and-electricity-demand-4.png)
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![FIGURE 1. Estimation of energy demand in the industrial sector [3].](publication/342434540/figure/fig1/AS:906123251036161@1593048026216/Estimation-of-energy-demand-in-the-industrial-sector-3_Q320.jpg)
![FIGURE 2. Population growth versus energy and electricity demand [4].](publication/342434540/figure/fig2/AS:906123251023872@1593048026306/Population-growth-versus-energy-and-electricity-demand-4_Q320.jpg)
![FIGURE 3. Strands of (a) ACAR (b) ACSR wires [22]](publication/342434540/figure/fig3/AS:906123251040257@1593048026342/Strands-of-a-ACAR-b-ACSR-wires-22_Q320.jpg)
![FIGURE 9. Line temperature with or without forest fire [93].](publication/342434540/figure/fig5/AS:906123251023873@1593048026431/Line-temperature-with-or-without-forest-fire-93_Q320.jpg)
The overhead transmission line system is one of the methods of transmitting electrical energy at a high voltage from one point to another, especially over long distances. The demand for electrical energy is increasing due to the increase in the world population, the evolution of transport technology, and economic expansion, thereby resulting in ove...
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Context 1
... and the energy demand is predicted to increase exponentially in the future. The increase in energy demand is also related to the increased use of electrical appliances in households, social activities, lifestyle, and climate change. Meanwhile, the global industrial sector energy consumption has increased significantly since 2006, as shown in Fig. 2 [4], because of the Fourth Industrial Revolution. This situation will produce a tremendous impact on energy demand, which is predicted to reach as high as 71961 ZW in ...
Citations
... Ensuring their safety is an unavoidable issue. Te layout of the monitoring system in the power grid can fnd accidents in the line in time and repair them as soon as possible, which can reduce the loss of power grid outages [18][19][20][21][22][23][24]. Based on the mechanical monitoring of transmission lines, Quaia et al. [25] proposed an algorithm for automatically detecting conductor breakage that needs to be used with sensors. ...
Conductor breakage with ice load is one of the major threats to the safe operation of transmission lines. The ice load increases the unbalanced longitudinal tension, leading to failure of tower members and even progressive collapse of the transmission line. This paper proposes an autotriggered anticollapse fusing hardware (AAFH), designed to reduce the unbalanced longitudinal tension caused by conductor breakage. When the longitudinal unbalanced tension of the transmission line exceeds the threshold of the AAFH, the fused part is destroyed, and the AAFH is elongated to reduce the longitudinal unbalanced tension. First, the construction and working mechanism of the device are introduced, and a numerical model of the transmission line–AAFH system is established to verify its effectiveness. Then, a parameter determination method for unbalanced tension in the tower-line system subjected to conductor breakage is proposed. In addition, the control performance of the device is studied. The results show that AAFH can effectively reduce the unbalanced tension induced by conductor breakage. The proposed method can predict the unbalanced tension of transmission lines, with an error within 10%. The greater the length of vertical/horizontal elongation, the better the protective effect. From a safety perspective, the AAFH should be designed according to the actual transmission line parameters to achieve an ideal control effect.
... OHTLs transport electrical energy from power-generating sources to load centers. Historically, the backbone of the power transmission system has included power plants, OHTLs, and substations, many of which were installed several decades ago and are now approaching or exceeding their designed operational lifespans [1]. This aging infrastructure leads to problems such as corrosion and material degradation, increasing maintenance costs and potentially posing risks to system reliability and safety [1]. ...
... Historically, the backbone of the power transmission system has included power plants, OHTLs, and substations, many of which were installed several decades ago and are now approaching or exceeding their designed operational lifespans [1]. This aging infrastructure leads to problems such as corrosion and material degradation, increasing maintenance costs and potentially posing risks to system reliability and safety [1]. Condition monitoring and maintenance of OHTLs to prevent faults is challenging but essential for system reliability. ...
... Power companies face challenges in automating power line condition monitoring and maintenance, primarily due to the limitations of conventional techniques, which are costly, time-consuming, and pose electrical hazards [27,28]. Addressing these limitations is crucial to avoid power outages and ensure OHTL reliability [1]. However, diverse and high-quality data are essential for advanced techniques like machine learning and computer vision. ...
Overhead transmission line insulators are non-conductive materials that separate conductors from grounded transmission towers. Once in operation, they frequently experience environmental pollution and electrical or mechanical stress. Since adverse operational conditions can lead to insulation failure, regular inspections are essential to prevent power outages. To this end, this paper proposes a novel technique based on deep convolutional neural networks (CNNs) to classify high-voltage insulator surface conditions based on their image. Successful applications of CNNs in computer vision have led to several pretrained architectures for image classification. To use these pretrained models, a practitioner typically fine-tunes and selects one final model via a model selection stage and discards all other models. In contrast with many existing studies that use such a “winner-takes-all” approach, here, we identify the best subset of seven popular pretrained CNN architectures that are combined by soft voting to form an ensemble classifier. From a machine learning (ML) perspective, this focus is warranted because the convolutional base of each pretrained architecture operates as a feature extractor and an ensemble of them works as a combination of various feature extraction rules. Our numerical experiments demonstrate the advantage of the identified ensemble model to individual pretrained architectures.
... Ice accumulation on transmission lines poses a real threat to the stability of the power transmission network, and, therefore, regular detection of ice on transmission lines becomes essential. However, this detection is easily hampered by climatic features such as variations in visibility and very low image contrast, which poses problems with image processing-based detection techniques that can lead to false alarms or complete failure of detection [26,27]. Several methods have been applied to determine the monitoring of ice on power transmission lines, namely inspection by human eyes, vehicles, meteorological models, mechanical models, and the use of sensors. ...
Line icings on the power transmission lines are dangerous risks that may lead to situations like structural damage or power outages. The current techniques used for identifying ice have certain drawbacks, particularly when used in complex environments. This paper aims to detect lines on the top and bottom in PTLI with low illumination and complex backgrounds. The proposed method integrates multistage image processing techniques, including image enhancement, filtering, thresholding, object isolation, edge detection, and line identification. A binocular camera is used to capture images of PTLI. The effectiveness of the method is evaluated through a series of metrics, including accuracy, sensitivity, specificity, and precision, and compared with existing methods. It is observed that the proposed method significantly outperforms the existing methods of ice detection and thickness measurement. This paper uses average accuracy of detection and isolation of ice formations under various conditions at a percentage of 98.35, sensitivity at 91.63%, specificity at 99.42%, and precision of 96.03%. Furthermore, the accuracy of the ice thickness based on the thickness measurements is shown with a much smaller RMSE of 1.20 mm, MAE of 1.10 mm, and R-squared of 0.95. The proposed scheme for ice detection provides a more accurate and reliable method for monitoring ice formation on power transmission lines.
... Line rating methods have been proposed since the 1970s [6]. They are usually differed by (i) the methodology applied to obtain the heat contribution equations and (ii) by the quality of the meteorological inputs affecting the line. ...
... The overhead catenary calculation takes into the maximum value of the temperature allowed by the cable. It is performed by either the use of auxiliary hardware (e.g: sensors or on-site meteorological stations) and most recently by the acquisition of weather and environmental data using external measurement components provided by satellite readings and sophisticated mathematical climate models on the geographical position of the line [3] [6]. In this project, weather data is obtained from the API of the meteomatics weather company and processed by models that follow a post-processing procedure of downscaling, using NASA's digital topographic model increase the spatial resolution to 90 meters. ...
This work aims to implement a Digital Twin model in dynamic line rating (DLR), using real-time and predictive environmental conditions to calculate the ampacity of overhead transmission and distribution lines (OHL). The study focuses on two transmission lines, "Betim6-Neves 1 and Betim 6-Barreiro" in the Belo Horizonte region of Brazil, spanning approximately 18.8 and 37.1 km, with a voltage level of 345 kV, 60 Hz. The modeling considered all constructive aspects, such as voltage level, frequency, geographic location, terrain topography, tower design, hardware, and characteristics of insulators and conductors. These parameters build a Digital Twin model to calculate ampacity in real-time and predictively. Meteorological data from a climate provider, essential for dynamic capacity calculations, were used according to Cigré 601 guidelines, which consider air temperature, wind speed, wind direction, and solar radiation. The primary contribution is a Web application for Transmission System Operator (TSO) and Distribution System Operator (DSO) operators, providing dynamic line rating information and a seven-day forecast based on meteorological variations. This tool is crucial as extreme weather conditions increasingly challenge the operation of electrical energy transmission systems. Additionally, the project includes a 3D model of the OHL, showing the right-of-way layout, tower locations, hardware details, and conductor positions, accurately reflecting the OHL's characteristics in a digital model. The Digital Twin uses real-time data from the Supervisory Control and Data Acquisition (SCADA) system of Cemig's operating center, empowering operators to optimize assets, reduce costs, and minimize carbon emissions. This remote, scalable, and environmentally friendly solution eliminates the need for additional hardware. The operations center operates securely, with strict cybersecurity measures and a dedicated connection between SCADA and data lake, ensuring data integrity and efficient information flow without exposing the operation center to cyber vulnerabilities. Thus, this innovative solution maintains efficiency and fluidity, essential for the operation of the system.
... Overheated transformers and generators face risks of winding damage and core degradation, while circuit breakers and switchgear may malfunction, jeopardizing system safety and reliability [11], [12]. Thermal expansion in overhead lines can cause sagging and faults due to reduced clearance, and repeated thermal cycling induces mechanical stress in transformers and motors, leading to structural damage [13], [14]. ESSs such as batteries and capacitors experience accelerated aging under high temperatures, reducing their effectiveness [15]. ...
The issue of power quality (PQ) has become increasingly critical in modern power systems due to the growing integration of sensitive electronic devices, electric vehicles, and renewable energy sources. Among the various PQ concerns, harmonics have emerged as a significant contributor to the overheating of power system components. This overheating phenomenon can have detrimental effects, including reduced equipment lifespan, increased maintenance costs, and unexpected failures. This comprehensive review paper provides an in-depth examination of the impact of harmonics on power system components, with a focus on the overheating problems they induce. The paper delves into an analysis of PQ indices and standards, underscoring the necessity for efficient mitigation techniques to address harmonic-related overheating and other PQ concerns. The review explores the key PQ devices employed to mitigate harmonics, such as passive power filters, active power filters, and advanced devices like D-STATCOM, unified PQ conditioners, and dynamic voltage restorers. Furthermore, it investigates the various control algorithms and optimization techniques utilized in the design of these power filters, emphasizing their pivotal role in addressing harmonics and preventing overheating. It also discusses the applicability of artificial intelligence techniques in mitigating power harmonics and improving PQ in modern power grid studies.
... The European Union also emphasizes the importance of increasing cross-border transmission capacities, which is crucial for the integration of electricity markets. According to the Agency for the Cooperation of Energy Regulators, providing maximum transmission capacity for electricity trading is an essential condition for achieving the targets for RES development [1,2]. ...
The examination of ampacity in overhead transmission lines offers a comprehensive overview, covering its definition, thermal evaluation methodologies, standards, practical applications, and the potential of high-temperature low-sag (HTLS) conductors. By navigating through static and dynamic approaches to thermal evaluation and detailing methodologies prescribed by international standards like those from CIGRE and IEEE, this study provides a solid understanding of how ampacity is determined and optimized. Furthermore, the exploration of HTLS conductors introduces a forward-looking perspective on enhancing transmission capacity while mitigating the need for extensive infrastructure modifications. Through an analysis of regional considerations and preferences using Slovak standards and CIGRE Technical Brochure 601, insights are provided into how environmental factors influence transmission line performance. The analysis of the transition from traditional ACSR (Aluminium Conductor Steel-Reinforced) to advanced ACCC (Aluminium Conductor Composite Core) conductors demonstrates the tangible benefits of adopting advanced conductor technologies in real-world scenarios.
... Literature [18] discusses the application of PTL technology in transmission line monitoring, makes an in-depth analysis of the aspects of three-dimensional reconstruction technology of PTL, target detection technology, and robot control, and finally looks forward to the development prospect of the inspection robot of power transmission system. Literature [19] describes the research progress of Overhead Line (OHL) monitoring technology, which helps transmission system researchers and managers to operate and make decisions when carrying out transmission system condition monitoring work. Literature [20] discusses the key role played by insulators in transmission lines and proposes a UAV insulator scale assessment scheme based on machine learning algorithms, which effectively supports managers in the assessment of insulator gutter conditions. ...
In order to improve the defect recognition efficiency of transmission lines, the industry is currently using aerial images for automatic visual defect detection to ensure the safe operation of transmission lines. This paper proposes a method for defect recognition from coarse to fine, based on convolutional neural networks and connected domain algorithms, to improve recognition accuracy. The recognition speed is improved by using the knowledge distillation method of target detection networks based on decoupled features, adversarial features, and attention features. It has been found that the optimized recognition model improves the precision rate by 7%, the recall rate by 8%, and the average accuracy rate by 10%. The FPS of the model optimized by knowledge distillation is 62.5, and the average value of the FPS of other versions of this model is 47.35. It is believed that the two optimization ideas introduced in this paper can enhance the previous transmission line defect recognition algorithm in terms of accuracy and recognition speed.
... Double-thermal overload (dtro) involves two components or circuits experiencing excessive heating, potentially indicating a broader system issue or simultaneous high-load conditions in multiple areas. Triple-thermal overload (ttro) signifies that three components or circuits are overheating, suggesting a systemic problem with the power system, cooling failure, or widespread environmental stress [166]. ...
Industry 4.0, the Fourth Industrial Revolution, is transforming manufacturing by integrating technologies like cyber-physical systems, the Internet of Things (IoT), and Artificial Intelligence (AI) into traditional practices. This revolution introduces a new era of automation, connectivity, and intelligence, enhancing manufacturing with improved flexibility, efficiency, and customization. Central to this transformation are three-phase electrical systems that power most manufacturing facilities worldwide. These systems, known for their reliability and efficiency, are essential for powering heavy machinery and supporting large-scale operations, becoming increasingly vital as industries push towards greater automation and sustainability.
Integrating AI into three-phase electrical systems introduces complex challenges across technological, operational, and strategic domains. Essential for smarter industrial operations, these systems require seamless AI enhancements to improve functionality. This integration demands significant technological adaptations for managing vast data volumes and transitioning from traditional to AI-driven maintenance strategies. Robust, real-time monitoring and responsive control systems are necessary to handle precise synchronization and balance, as minor discrepancies in voltage or frequency can cause major efficiency losses and equipment damage. Moreover, achieving this integration without disrupting existing operations calls for innovative solutions adaptable to varying conditions.
This thesis introduces a cutting-edge system architecture designed to integrate Artificial Intelligence (AI) and the Industrial Internet of Things (IIoT) within three-phase electrical systems, crucial for modern industrial operations. The system comprises Power Monitoring and Control Units (PMCU), a Main Control Unit (MCU), and a Main Power Monitoring Unit (MPMU), each playing a vital role in enhancing the functionality and responsiveness of the overall system. By incorporating advanced monitoring and control technologies, this architecture allows for real-time analytics and decision-making capabilities, pivotal for dynamic operational environments.
The first major contribution of this system is the implementation of machine learning and deep learning models tailored to improve fault detection processes. These models are meticulously designed to analyze complex data streams and identify potential system faults with high precision and speed. Validation against extensive fault scenario datasets ensures that these models are both accurate and robust, capable of detecting subtle anomalies that might otherwise go unnoticed, thereby significantly enhancing the system's diagnostic capabilities.
Another significant contribution is the development of AI-driven methodologies for optimizing load balancing and fault correction. These methodologies leverage real-time data to dynamically manage loads and redistribute power as needed to maintain system stability and efficiency. The inclusion of adaptive control algorithms allows for the continuous adjustment of system parameters, effectively minimizing energy waste and optimizing operational performance. This dynamic load management system has been rigorously tested through both simulations and real-world applications, demonstrating marked improvements in energy efficiency and system reliability.
Furthermore, the research advances the use of AI for dynamic phase angle adjustment, which is crucial for maintaining efficient power transfer and minimizing operational disruptions. By implementing algorithms that can adjust phase angles in real-time based on current load conditions, the system can respond more effectively to changes in demand and operational stress, ensuring optimal performance. These capabilities have been extensively validated in industrial settings, showcasing their ability to significantly enhance the system's operational efficiency and stability
... In addition to environmental factors, the choice of conductor material plays a crucial role in determining the reliability of transmission wires. The most commonly used types include all-aluminum conductor (AAC), all-aluminum alloy conductor (AAAC), and aluminum conductor steel-reinforced (ACSR) [13]. ...
The failure of overhead transmission lines in the United States can lead to significant economic losses and widespread blackouts, affecting the lives of millions. This study focuses on the reliability of transmission lines, specifically examining the effects of wind, ambient temperature, and current demands on lines, incorporating minimal and significant pre-existing damage. We develop a Thermo-Electro-Mechanical Model to analyze the transmission line failures across sensitive and affected states of the United States, integrating historical data on wind and ambient temperature. By combining numerical simulation with historical data analysis, our research assesses the impact of varying environmental conditions on the reliability of transmission lines. Our methodology begins with a deterministic approach to model temperature and damage evolution, using phase-field modeling for fatigue and damage, coupled with electrical and thermal models. Later, we adopt the Probability Collocation Method to investigate the stochastic behavior of the system, enhancing our understanding of uncertainties in model parameters, conducting sensitivity analysis, and estimating the probability of failures over time. This approach allows for a comprehensive analysis of factors affecting transmission line reliability, contributing valuable insights into improving power line's resilience against environmental conditions.
... In recent decades, the global demand for electrical energy consumption has generally increased, due to rapid population growth, the development of industrialization and economic developments. Therefore, to meet these energy needs, high and extra high-voltage (HV and EHV) power transmission lines are established by single and double circuit configurations, and significant improvements are made to existing power networks, in order to significantly increase production capacity [1][2][3][4][5][6]. These AC transmission power lines generate high levels of industrial frequency electric and magnetic fields, making it a source of significant concern to the international scientific community regarding the potential adverse effects on human health and the environment that may result in the long term from continuous exposure to these fields. ...
In this paper, a quasi-static modelling for assessment and screening of electric field generated by an EHV overhead alternating current (AC) transmission line installed adjacent to a sensitive area is treated, based on an improved computing paradigm that is a hybrid charge simulation method (CSM) and grasshopper optimization algorithm (GOA). The intelligent algorithm is applied to adjust the optimal number and position of simulating changes to improve the accuracy of the electric field calculation method, as well as to determine the geometric coordinates of the passive and active shielding conductors and to evaluate the voltage to be injected in the case of active shielding; to significantly reduce the electric field in the area to be protected. The results showed that when the sag effect is considered, the electric field value at mid-span is higher than in the vicinity of the suspension pylons, the average value can be considered approximately equal to the electric field value, when the sag effect is neglected. The performance of screening effect shows that active shielding is more reliable than passive shielding for electric field attenuation. An undesirable side effect for transmission line energy efficiency arises from the shielding wires which consist of the increase in the maximum gradient of the electric field and the energy losses by Corona phenomena on the surface of the conductors. The methodology adopted performance is in perfect agreement with the results reported in the CIGRE (International Council on Large Electric Systems) standard.
