Transmission Line Monitoring System Research Articles

Combination of a Rabbit Optimization Algorithm and a Deep-Learning-Based Convolutional Neural Network–Long Short-Term Memory–Attention Model for Arc Sag Prediction of Transmission Lines

Arc droop presents significant challenges in power system management due to its inherent complexity and dynamic nature. To address these challenges in predicting arc sag for transmission lines, this paper proposes an innovative time–series prediction model, AROA-CNN-LSTM-Attention(AROA-CLA). The model aims to enhance arc sag prediction by integrating a convolutional neural network (CNN), a long short-term memory network (LSTM), and an attention mechanism, while also utilizing, for the first time, the adaptive rabbit optimization algorithm (AROA) for CLA parameter tuning. This combination improves both the prediction performance and the generalization capability of the model. By effectively leveraging historical data and exhibiting superior time–series processing capabilities, the AROA-CLA model demonstrates excellent prediction accuracy and stability across different time scales. Experimental results show that, compared to traditional and other modern optimization models, AROA-CLA achieves significant improvements in RMSE, MAE, MedAE, and R2 metrics, particularly in reducing errors, accelerating convergence, and enhancing robustness. These findings confirm the effectiveness and applicability of the AROA-CLA model in arc droop prediction, offering novel approaches for transmission line monitoring and intelligent power system management.

Design of intelligent detection method for electricity transmission line equipment defect based on data mining algorithm

Electricity transmission line is the most significant way of power transmission. Regular detection of it can find and eliminate its defects and hidden dangers in time and prevent major accidents, which is of great significance to the power system. In order to find the problems in the electricity transmission line in time, this paper applied the data mining algorithm to the intelligent detection method of electricity transmission line equipment defects. An electricity transmission line equipment defect intelligent detection and monitoring system was constructed, and the differences between clustering analysis image recognition technology in data mining algorithms and the XGBoost algorithm were analyzed. The results showed that compared with using XGBoost algorithms, the highest accuracy rate of the intelligent detection method of electricity transmission line equipment defects using data mining algorithm in the detection results was 98 %, which was generally higher than that of XGBoost algorithms, and could reduce the consumption of time. From the perspective of replication rate, the overall average value of XGBoost algorithms was 52.38 % and the overall average value of data mining algorithms was 7.63 %. The replica rate of data mining algorithm was much lower than that of XGBoost algorithm and the performance of fault signal detection was better. Therefore, the application of data mining algorithm to the intelligent detection method of electricity transmission line equipment defects can be more suitable, thus significantly improving the efficiency of all aspects. At the same time, the method has the advantages of simple operation, fast, reliable and not affected by region.

Retracted: Application of AI Image Recognition in the Omnidirectional Monitoring System for Power Transmission Lines

Retracted: Application of AI Image Recognition in the Omnidirectional Monitoring System for Power Transmission Lines

Information Monitoring of Transmission Lines Based on Internet of Things Technology

To solve the problem of difficult real-time monitoring of current transmission lines, this article proposes an information-based monitoring system for transmission lines based on Internet of Things technology. The system utilizes the characteristics of strong scalability, good fault tolerance, low power consumption, and low cost of the Internet of Things. Taking the ultra-low power consumption MSP430 microcontroller and CC2430 radio frequency module as the core, a line monitoring system based on the Internet of Things is designed. The proposed design uses ZigBee wireless sensor network technology which is powered by solar energy. The collection, transmission, processing and judgment of various environmental parameters of the line are realized. The data information is transferred to the monitoring center of the upper computer through GPRS. When there is an abnormality, it can send a mobile phone short message to the person in charge to feedback the abnormal content in time. The distribution network's load symmetry allowed for the development of several locating procedures. For the three-phase symmetric scheme, the fault location approach based on line supply characteristics was employed, and for the three-phase asymmetric scheme, the fault location technique based on line impedance is proposed. One of the most vital uses for the Internet of Things is in the mitigation of power transmission line failures and disasters. Improved power transmission dependability, less financial loss, and fewer power outages are all possible thanks to the Internet of Things' cutting-edge sensing and communication technology. This research introduced the use of IoT in online monitoring system of electricity transmission line with a focus on the characteristics of the construction and development of smart grid. The results indicated that the system's highest temperature difference is 0.31 C, while the maximum humidity difference is 1.38%. The system increases the safety and manageability of electricity transmission while also fostering the widespread adoption and technical integration of the smart grid and the Internet of Things.

Design of mountain fire prevention monitoring system for transmission lines based on machine vision algorithms

Abstract With the continuous development of society, power grid has become a major guarantee for social and economic development, and its coverage is also expanding. The power grid not only covers some developed areas, but also extends to remote mountainous areas with complex changes in geological and climatic conditions. However, at the same time, line failures and power outages caused by mountain fires are becoming increasingly frequent, which poses a significant threat to the safety and stable operation of the power system. Therefore, how to solve the problem of mountain fire on transmission lines has become an important topic today. The main purpose of this study is to achieve effective monitoring of transmission line mountain fires and provide more assurance for the stable operation of the power system. This article discussed the transmission line and traditional mountain fire prevention monitoring system, and combined the current hot machine vision algorithm to design the overall mountain fire prevention monitoring system, focusing on analyzing the differences between this system and traditional systems. Through experiments, it was found that the monitoring accuracy of the mountain fire prevention monitoring system designed in this article has been improved by 10.6 % compared to the traditional system at location 4, 7.6 % at location 6, and 7.9 % at location 7. At the same time, the real-time performance and anti-interference performance of this system were also better than traditional systems, and the overall performance of the system was better. Practice has proved that the system can effectively overcome the interference of external environment, and monitor and identify the fire situation in real time and accurately, ensuring the safety of transmission lines. At the same time, the research on machine vision algorithms in this paper can also help promote the more perfect development of power systems. Practice has proved that this system can effectively overcome the interference of the external environment, and continuously and accurately detect and identify the fire situation.

Anomaly detection based on lightweight deep learning algorithms

The safe and reliable operation of power transmission lines is the key to the sustainable and stable operation of power grids. Using the artificial intelligence techniques for the channel status monitoring greatly improves the inspection efficiency of transmission lines. However, the discrimination is mostly conducted on the cloud side, which does not meet the requirement of real-time processing. In this paper, we build an intelligent monitoring system for transmission lines based on edge computing. According to the specific characteristics of the real-world power filed applications, a lightweight deep learning algorithm model is designed. It is then transplanted on a self-developed AI chip and a field test is carried out. The experimental results show that the model designed in this paper balances well between the accuracy, the model size and the inference speed. It realizes status local discrimination of the transmission lines, and promising detection results are obtained.

Study on the enhancement method of online monitoring image of dense fog environment with power lines in smart city.

In this research, an image defogging algorithm is proposed for the electricity transmission line monitoring system in the smart city. The electricity transmission line image is typically situated in the top part of the image which is rather thin in size. Because the electricity transmission line is situated outside, there is frequently a sizable amount of sky in the backdrop. Firstly, an optimized quadtree segmentation method for calculating global atmospheric light is proposed, which gives higher weight to the upper part of the image with the sky region. This prevents interference from bright objects on the ground and guarantees that the global atmospheric light is computed in the top section of the image with the sky region. Secondly, a method of transmission calculation based on dark pixels is introduced. Finally, a detail sharpening post-processing based on visibility level and air light level is introduced to enhance the detail level of electricity transmission lines in the defogging image. Experimental results indicate that the algorithm performs well in enhancing the image details, preventing image distortion and avoiding image oversaturation.

Research on online monitoring and early warning system of transmission line galloping based on multi-source data

When the conductor is covered with ice on a non-circular section, and there is low-frequency, large-amplitude motion under wind excitation, it is usually called the transmission line galloping phenomenon. Due to the large amplitude, the galloping of the transmission line will lead to line fatigue, increase in tension, damage to hardware, or toppling of towers. In addition, it will also lead to flashover, tripping, and other transmission accidents caused by the short phase distance, which is not conducive to the safe operation of power grids. In order to improve the safety of power grid operation, this paper designed an online monitoring and early warning system for transmission line galloping on the basis of multi-source data and verified the system through implementation. It was found that the safety factor was 1.568, which determines its early warning level and affirms the feasibility of this study. This research has a positive role in improving the safe operation of the power grid.

Design of a Power Transmission Line Monitoring System Based upon Edge Computing and Zigbee Wireless Communication

Transmission line detection is a magic weapon to protect power safety, and it plays an irreplaceable role in stabilizing electricity use. However, the detection of transmission lines contains complex objects and rich content, which puts forward a new test for the accurate identification and power consumption of the monitoring. The advantages of Zigbee wireless communication technology are low energy consumption, simple operation, and strong cost control. It is a key to cracking the high energy consumption of transmission line detection. Edge computing is a supplement to cloud computing. It has the characteristics of low bandwidth and low latency, which is conducive to cracking the complex scenarios of wireless monitoring. This article was aimed at studying and designing a monitoring system for transmission lines to ensure the orderly operation of remote monitoring of transmission lines. This paper proposes to use Zigbee wireless communication as the core and integrate edge computing to construct a wireless transmission line sensing system to form a remote transmission line detection system. This paper builds a detection system, systematically tested the system’s energy consumption, anti-interference, fault monitoring recall, and system accuracy, and evaluated the overall operation of the system as a whole. The experimental results in this paper show that the monitoring and attraction of transmission lines can effectively reduce the risk of circuit failure by 20%.

IoT Based Transmission Line Monitoring System

Electrical power is transmitted from the generation plants to end-users at distant locations through the transmission lines. These lines are exposed to various environmental conditions and faults may occur causing power interruption to end users, damage to the power system. The fault detection has been a main objective of power system engineers, in transmission and distribution systems. Identification of fault source is tedious task; fast fault detection can help to protect the equipment before any significant damage of the equipment. The exact fault dictation can help service man to remove persistent of the faults and type the faults occur regularly, thus reducing the occurrence of fault and minimize the time of power outages. The paper is intended to detect the of fault in transmission line using an NodeMCU and the same is transmitted to control center using IoT device.

Self-powered Wireless Online Monitoring System for Electric Transmission Lines

Self-powered Wireless Online Monitoring System for Electric Transmission Lines

Application of AI Image Recognition in the Omnidirectional Monitoring System for Power Transmission Lines

With the continuous advancement of social economy, electric power, as an essential resource, plays an important supporting role for the development of industry and the lives of residents. As electric power itself is dangerous to a certain level, it is crucial to use the electric power rationally and ensure the safe application of electrical equipment. In this paper, AI image recognition technology is introduced to establish a comprehensive monitoring system for power transmission lines. Through the control of the potential risks of the transmission lines, emergency treatment of power transmission lines is implemented, which has provided an application support for the safe use of power equipment. Simulation experiments have verified that the image recognition technology based on AI can support omnidirectional monitoring of power transmission lines effectively.

Physical Simulation of the Spectrum of Possible Electromagnetic Effects of Upward Streamer Discharges on Model Elements of Transmission Line Monitoring Systems Using Artificial Thunderstorm Cell

The results of a physical simulation using negatively charged artificial thunderstorm cells to test the spectrum of possible electromagnetic effects of upward streamer discharges on the model elements of transmission line monitoring systems (sensor or antennas) are presented. Rod and elongated model elements with different electric field amplification coefficients are investigated. A generalization is made about the parameters of upward streamer current impulse and its electromagnetic effect on both kinds of model elements. A wavelet analysis of the upward streamer corona current impulse and of the signal simultaneously induced in the neighboring model element is conducted. A generalization of the spectral characteristics of the upward streamer current and of the signals induced by the electromagnetic radiation of the nearby impulse streamer corona on model elements is made. The reasons for super-high and ultra-high frequency ranges in the wavelet spectrum of the induced electromagnetic effect are discussed. The characteristic spectral ranges of the possible electromagnetic effect of upward streamer flash on the elements of transmission line monitoring systems are considered.

DEVELOPMENT, DESIGN AND APPLICATION OF UNMANNED AERIAL VEHICLES FOR POWER LINES

The article reviews the methods for improving the technology of monitoring and inspection of the state of power grids. The state of development in the design and methods of using unmanned aerial vehicles, which are intended for servicing power lines, is analyzed. A number of issues related to the concept of unmanned aerial vehicles with high lifting capacity are considered, which allows them to be used for various means of maintenance of power lines, which in turn reduces capital costs. Unmanned aerial vehicles are increasingly being used by utilities, which reduces the risks and costs of maintenance compared to other transmission line monitoring systems. It was determined that in the field of electric power industry the most promising of the methods for monitoring power lines is the application of unmanned aerial vehicles together with the use of modern measuring equipment and digital technologies. It is concluded that a significant advantage of the use of unmanned aerial vehicles is their possibility, which reduces costs and increases the efficiency of their use as a means of determining promising locations for wind farms. It is noted that with the transition of the global electricity industry to the use of unmanned aerial vehicles will save resources and reduce costs. Moreover, it is determined that the use of unmanned aerial vehicles with high lifting capacity allows minimizing the cost of using additional lifting equipment and technical personnel. In particular, high-lift unmanned aerial vehicles using flamethrowers can effectively remove obstacles such as clogging on power lines. It is noted that the use of unmanned aerial vehicles will increase the safety of employees in the maintenance of power facilities and power lines. The obtained results from the analysis of the technical condition of the use of unmanned aerial vehicles indicate the prospects for the introduction of remote control systems and monitoring of the operational condition of power lines.

The Research and Implementation Transmission Line Tower Rod and Monitoring System Using Reverse Network RTK Technology

In order to solve the problems of low monitoring accuracy, high false alarm rate, untimely pre-warning and high equipment failure rate of the transmission line tower rod oblique posture monitoring system, a transmission line tower rod oblique monitoring system based on reverse network RTK was developed. The system integrated the Beidou multi-source sensor, network communication and prediction and forewarning, used the height and millimeter level position information of the transmission line tower to measure and calculate oblique posture information of the tower pole, and made risk assessments, so as to realize the control of these information. The actual tests results showed that the monitoring system could accurately obtain the oblique posture information of the transmission line tower pole, and the pre-alarming monitoring results met the requirements of intelligent, accurate and digital management of transmission line tower pole.

Time-Multiplexed Self-Powered Wireless Current Sensor for Power Transmission Lines

Current measurement is a key part of the monitoring system for power transmission lines. Compared with the conventional current sensor, the distributed, self-powered and contactless current sensor has great advantages of safety and reliability. By integrating the current sensing function and the energy harvesting function of current transformer (CT), a time-multiplexed self-powered wireless sensor that can measure the power transmission line current is presented in this paper. Two operating modes of CT, including current sensing mode and energy harvesting mode, are analyzed in detail. Through the design of mode-switching circuit, harvesting circuit and measurement circuit are isolated using only one CT secondary coil, which eliminates the interference between energy harvesting and current measurement. Thus, the accurate measurement in the current sensing mode and the maximum energy collection in the energy harvesting mode are both realized, all of which simplify the online power transmission line monitoring. The designed time-multiplexed working mode allows the sensor to work at a lower transmission line current, at the expense of a lower working frequency. Finally, the proposed sensor is verified by experiments.

Typhoon Inversion Method Combined With the YanMeng Wind Field and a Meteorological Monitoring System for Transmission Lines

Extreme weather like typhoon leaves significantly terrible influence on the stability of power grid in the coastal area. A high-voltage transmission line includes hundreds of towers and usually stretches hundreds of kilometers. It is not economical to install typhoon monitoring system throughout the transmission line. This paper proposes a typhoon inversion method (TIM) for transmission line which utilizes engineering wind field model and only requires monitoring data from a few towers. The developed method is called YanMeng wind field (YM) with directional mutation genetic algorithm (DMGA) for transmission lines, or YM-DMGA method. DMGA utilizes real-time monitoring data, and improves simulation accuracy of average wind speed by dynamically optimizing two critical parameters <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z_{0}$ </tex-math></inline-formula> in wind field model, which is a great improvement over the traditional YM model relying on historical data. The TIM is mainly composed of three parts, the meteorological monitoring system (MMS), the YM-DMGA model, and the software system. The TIM collects real-time measured wind data of particular monitoring stations through the MMS. Meanwhile, wind data of the whole transmission line is simulated using the YM-DMGA and displayed by the software system. Then the method proposed is verified to be effective in three aspects. First of all, the YM-DMGA method has great enhancement in accuracy, for the coefficient of determination <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{square}$ </tex-math></inline-formula> increases from 0.811 to 0.986. Secondly, DMGA in the method has quicker convergent speed than typical GA, with fitness converged in the 269 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> epoch, earlier than that for typical GA, 656 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> epoch. Lastly, DMGA has better optimal fitness value of 0.6239, bigger than that of typical GA, 0.5891. At the end of the paper, an application of the method to an 110kV double-circuit transmission line is presented to reveal the risk under the impact of the super typhoon Rammasun.

Secure Transmission Lines Monitoring and Efficient Electricity Management in Ultra-Reliable Low Latency Industrial Internet of Things

Smart Grid (SG) faces several challenges to efficiently transfer the power generated to power consumers. So, a robust monitoring tool is required to monitor the transmission lines in order to ensure security of the resource. This power transmission monitoring is a good example of ultra reliable low latency application of 5G with the aim to provide quality of service and quality of experience. The primary objective of this study is to design a wireless network for real-time monitoring of transmission lines to take the preventive measures. In this paper, we present an Internet of Things enabled real-time transmission line monitoring system comprising of wireless, wired, and cellular technologies. The objective is to minimize the time delay at minimum installation cost of the network. In our proposed model, all the sensors are powered by Renewable Energy Resources (RES) like wind and solar energy, etc. The placement problem is formulated to determine the location of cellular enabled transmission towers. Moreover, feasible regions are also calculated to show the relationship between time delay and energy consumption. Results show that proposed model provides efficient solutions and takes less time for data transmission and is more energy efficient.

Research on A New On-line Monitoring System of Transmission Line Icing Based on RF433 Technology

In view of the disadvantages of the current transmission line icing online monitoring terminal data transmission through wire and large power consumption, This paper uses RF433?WIFI and low-power design technology, realize the low-power wireless transmission function of each sensor data, and through the communication protocol between the front-end wireless sensor and the main controller, achieve the purpose of improving the efficiency of the sensor. Finally, the terminal efficiency designed in this paper is verified by actual test. After 72 hours of test, the packet loss rate of each sensor is 0%, which indicates that the system can run stably. At the same time, the maximum power consumption of the main control board in one day is 0.11W, which is far lower than the power consumption of the online monitoring terminal of the cable icing over 1W. In this paper, the design of low-power wireless icing monitoring system for transmission lines can correctly guide the work of anti-icing and anti-icing, which is of great significance for the safe operation of transmission lines.

Application of IoT in power transmission line monitoring

Nowaday in Vietnam, the verification and maintenance of 220kV - 500kV transmission lines is mainly done by manual labor. However, this task is quite hard, the workers have many difficulties such as the long moving distance (hundreds of kilometers) through many areas of complex terrain to verify the data and prevent the widespread problems. The efficiency and quality of line check also depends on the enthusiasm and experience of the workers. The demand to develop the power grid is constantly increasing to release full capacity from solar power plants, wind power plants, making the line always operating in overload state. Some problems are caused by the rapid growth of plants violating high voltage transmission line safety distances.Therefore, an online transmission line monitoring system will be a useful tool for improving the line operation and reduce the pressure for workers. In this paper, the application of the wireless data transmission platform and LABVIEW programming software to build a system for online data collection of lines, program settings, applications on mobile devices. Dynamic monitoring of lines over the Internet reduces the workload of workers.