Transmission Line Icing Research Articles

Numerical simulation of two-dimensional transmission line icing and analysis of factors that influence icing

The formation of ice on electrical transmission lines poses an increasingly substantial safety hazard. In this study, a two-dimensional transmission line icing model was established to predict the shape of the icing. The airflow field and convective heat transfer were calculated using Fluent software, and a discrete phase model (DPM) based on the Lagrange method was used to calculate the water droplet collection coefficient on the surface of the transmission line. The Messinger thermodynamic model was programmed using the user-defined function (UDF) of Fluent, and the icing shape and icing thickness on the surface were calculated. The effects of wind speed, median volume diameter (MVD) of water droplets, temperature, liquid water content, wind direction angle, and electric field intensity on the icing characteristics of transmission lines were considered. The forced vibration of the transmission line was simulated using the UDF, and its influence on icing characteristics was thoroughly analyzed. Furthermore, the influence law of the vibration amplitude and frequency on the local water drop collection coefficient and ice shape was determined. The results elucidate the effects of the aforementioned parameters on transmission line icing. These findings provide a reference for future efforts to mitigate the impact of transmission line icing. Compared with other studies, this study has considered the influencing factors more fully, predicted the ice shape under different conditions more comprehensively, and can be more pertinently applied to different regions with different meteorological conditions.

Discussion on the Range Configuration Strategy of Tension Sensor of Icing Monitoring Terminals of High-Voltage Transmission Line

Online monitoring of transmission line icing is an important means of anti-icing in the power grid. The accuracy mainly depends on the accuracy of the terminal tension sensor, which is one of the main sources of monitoring errors from large range configuration of tension sensor. This paper analyzes the working principle and characteristics of tension sensors and the load calculation method of different types of insulator hanging points to determine the output error of over-range configuration tension sensors. It finally proposes a principle and a calculation method for tension sensors’ range configuration, which is verified to be reliable by experiments. The results can provide a basis for the selection of tension sensors for icing-monitoring terminals and reduce the error caused by temperature drift and zero drift of tension sensors.

Machine learning-based ice detection approach for power transmission lines by utilizing FBG micro-meteorological sensors.

Severe icing of transmission lines causes power failures, tower collapses, and other adverse events, which hinders the normal operation of society. Existing icing monitoring methods have problems of irregular monitoring and poor accuracy. In this study, we propose a comprehensive model for predicting hard rime and glaze ice using temperature, humidity, and historical icing data. The results of the experimental verification conducted for nine icing cycles in different years and geographic locations demonstrate that the proposed technique can effectively predict multiple types of icing while ensuring correlation coefficients > 0.99 and mean squared error < 4%.

Experimental Study on Ultrasonic Detection Method of Ice Thickness for 10 kV Overhead Transmission Lines

Transmission-line icing is a serious threat to the security safe and stable operation of power systems. The icing monitoring of transmission lines is of significance for the prevention of freezing disasters in power systems. However, the lack of effective ice detection methods has brought great trouble to the safe operation for 10 kV overhead transmission lines in freezing weather. To tackle this problem, a method for detecting ice thickness for 10 kV overhead transmission lines based on ultrasonic detecting technique is proposed in this paper, in which the distance from the ultrasonic sensor to the line changed by transmission lines ice-coating and the distance difference before and after ice-coating detected by ultrasonic being the ice thickness. An ultrasonic detection platform for ice thickness on the steel rod to simulate the line icing is established. The calibration experiment with the effect of distance, temperature, and wind velocity on the ultrasonic is carried out, and the experiment with ice thickness on the steel rod detected by the ultrasonic is carried out. Furthermore, the effect of ice shape on the detection is studied, and the reason for the large error is analyzed when the number of bulges on the ice surface is large. The results show that the relative error of ice thickness obtained by the method proposed in this paper is less than 10% under conditions of uniform ice and relatively smooth ice surface, compared with vernier caliper measurements. An improved method for ice thickness detection using the trough time as the ultrasonic echo signal time is proposed when the number of bulges on the ice surface is large, and the relative error value of the improved method is verified to be less than 5%.

Retracted: Prediction of Transmission Line Icing Using Machine Learning Based on GS‐XGBoost

Retracted: Prediction of Transmission Line Icing Using Machine Learning Based on GS‐XGBoost

Icing Detection Method of Transmission Line Based on Machine Vision

The conventional icing detection method is mainly based on the manual detection of icing status. The texture correlation characteristics of icing are variables, which lead to large errors in the icing edge detection and affect the subsequent maintenance of transmission lines. Therefore, the transmission line icing detection method based on machine vision is designed in this paper. The method comprises the following steps of extracting the icing texture feature of the transmission line, namely extracting the correlation feature of an icing image, identifying an icing area by using machine vision technology, carrying out edge detection on the transmission line and finally obtaining the icing thickness of the transmission line to realize the accurate detection on the icing of the power transmission line. The comparative experimental results show that the detection error of this method is small, the detection accuracy is high, and it can be applied to real life.

Numerical Simulation of Transmission LineIcing Based on Computational Fluid Dynamics

In recent years, transmission line icing disasters occur frequently, which poses a great threat to the operation of power grid. Most of the existing studies do not consider the impact of time-varying meteorological conditions on conductor icing. In order to effectively predict the growth of line icing, it provides an important reference for transmission line deicing and galloping early warning. The wind tunnel model of the transmission line is established by using the computational fluid dynamics simulation software, and the air flow field distribution around the transmission line is calculated by using the hybrid grid division. By establishing the motion equation of cooling water droplets in the air and analyzing the motion trajectory of water droplets, the local collision coefficient between water droplets and the transmission line is obtained, and the icing surface of the transmission line is discretized into a control body, Through the establishment of thermodynamic model, the mass balance and energy balance equations are established to calculate the icing quality of transmission line. According to the environmental temperature, wind speed, transmission line diameter and other factors, the quantitative influence degree of different factors on the icing form of transmission line is output, so as to provide technical support for the safe and stable operation of important transmission channels.

Prediction of Transmission Line Icing Using Machine Learning Based on GS-XGBoost

In recent years, data have shown that transmission line icing is the main problem affecting the operation of power grids in bad weather; it greatly increases operating costs and affects people’s lives. Therefore, the development of a calculation method to predict the risk of ice on transmission lines is of great importance for the stability of the power grid. In this study, we propose a maximum mutual information coefficient (MIC) and grid search optimization extreme gradient boosting (GS-XGBoost) transmission line ice risk prediction method. First, the MICs between the ice thickness and the precipitation, wind speed, wind direction, relative humidity, slope, aspect, and elevation characteristic factors are calculated to filter out the effective features. Second, a grid search method is used to adjust the hyperparameters of XGBoost. The resulting GS-XGBoost model builds a prediction system based on the best parameters using a training set (70% of the data). Finally, the performance of GS-XGBoost is evaluated using a test set (30% of the data). For multiline, cross-regional icing data, our experimental results show that GS-XGBoost outperforms other machine learning methods in terms of accuracy, precision, recall, and F 1 score.

Study on transmission line icing prediction based on micro-topographic correction

For the requirement of refinement prediction of transmission line icing thickness, this paper presents an icing thickness correction method which combines the prediction using low spatial resolution with the correction of micro-topography features. By analyzing the regional characteristics of micro-topography, the areas can be divided into three categories: plains, high-altitude mountains, and low-altitude mountains. For each type of terrain, the quantitative correction model for the icing thickness is established, which is dependent on altitude, relief amplitude, slope angle, slope aspect, water system, valley line, ridge line, and saddle point. In this paper, according to the numerical prediction on the 3 × 3 km2 grids, the icing thickness of the transmission line on the 300 × 300 m2 grids with micro-topography is obtained using the correction method in Hubei Power Grid. The results show that the correction method is efficient and convenient to improve the accuracy of icing thickness prediction of the transmission line.

Probability prediction method of transmission line icing fault based on adaptive relevance vector machine

To precisely forecast the operation status of transmission line during an ice storm and achieve early warning, a method based on adaptive relevance vector machine (ARVM) is proposed for fault probability prediction of transmission line icing. According to the basic theory of RVM, this paper establishes a forecasting model, which consists of selection and preprocessing of data, initial parameter optimization, icing prediction with adaptive optimization and fault probability prediction of transmission line. The quantum particle swarm algorithm, together with K-fold Cross-validation is applied to optimize model parameters. The weight vector of the icing prediction model is corrected by repeating training to get the precise prediction result of ice thickness with its probability distribution. The case study with practical data from Zhejiang province shows that the proposed method can effectively improve the accuracy of icing prediction and further realize fault probability prediction of transmission line, which can provide early warning for the anti-icing and mitigation work of the electric power department.

Preventive Scheduling for Reducing the Impact of Glaze Icing on Transmission Lines

The icing on transmission lines threatens the power system security while the heat caused by power transmission losses can prevent icing growth. This paper proposes a preventive scheduling model for mitigating the glaze icing by optimizing the distribution of power losses on ice-coated transmission lines. An analytical glaze icing growth model is established based on the heat balance theory, which builds a direct relationship between icing growth and power transmission losses. Accordingly, the analytical glaze icing model is embedded into the proposed scheduling model to quantify the impact of power system schedules on transmission line icing and reduce the glaze icing of transmission lines. The scheduling model co-optimizes active power dispatch, demand response, and reactive power optimization for promoting the de-icing effect. To overcome the computational difficulties, the analytical glaze icing growth model is further linearized, and the Lagrangian relaxation method is adopted for identifying a practical solution. Case studies are conducted on different icing scenarios in the IEEE RTS-79 test system to verify the validity of the proposed model. Results show that the proposed preventing scheduling model can avoid the icing on transmission lines for mild ice disasters, while efficiently restraining the icing growth on transmission lines when they cannot be completely de-iced in severe storms.

Analysis of transmission line icing prediction based on CNN and data mining technology

Analysis of transmission line icing prediction based on CNN and data mining technology

Dynamic Simulation of Power Systems Considering Transmission Lines Icing and Insulators Flashover in Extreme Weather

Global climate deterioration caused by excessive carbon emissions poses a severe threat to the stable operation of power systems. In extreme snow and ice weather, complex faults, such as ice-covered line breakage and insulator flashover, may be triggered. Hybrid simulations to reveal the influence mechanism and dynamic interaction process between extreme weather and power systems have become a research focus. Relationship models between meteorological conditions and grid failure were constructed in ice accretion and insulator flashover scenarios. Based on the above two models, a multi-variable and multi-time scale hybrid simulation of the meteorological process and power system, considering line icing and insulator flashover, was realized. An example was provided to verify the feasibility of the proposed simulation scheme.

LightGBM Low-Temperature Prediction Model Based on LassoCV Feature Selection

Icing disasters on power grid transmission lines can easily lead to major accidents, such as wire breakage and tower overturning, that endanger the safe operation of the power grid. Short-term prediction of transmission line icing relies to a large extent on accurate prediction of daily minimum temperature. This study therefore proposes a LightGBM low-temperature prediction model based on LassoCV feature selection. A data set comprising four meteorological variables was established, and time series autocorrelation coefficients were first used to determine the hysteresis characteristics in relation to the daily minimum temperature. Subsequently, the LassoCV feature selection method was used to select the meteorological elements that are highly related to minimum temperature, with their lag characteristics, as input variables, to eliminate noise in the original meteorological data set and reduce the complexity of the model. On this basis, the LightGBM low-temperature prediction model is established. The model was optimized through grid search and crossvalidation and validated using daily minimum surface temperature data from Yongshan County (station number 56489), Zhaotong City, Yunnan Province. The root mean square error, MAE, and MAPE of the model minimum temperature prediction after feature selection are shown to be 1.305, 0.999, and 0.112, respectively. These results indicate that the LightGBM prediction model is effective at predicting low temperatures and can be used to support short-term icing prediction.

Prediction on freezing fraction and collision coefficient in ice accretion model of transmission lines using icing mass growth rate

Freezing fraction and collision coefficient are the key parameters in the ice accretion model, which should be calculated to predict the icing trend for transmission lines. Here a mathematical relation between freezing fraction and collision coefficient was derived using the simplified head balance model of transmission lines icing. Combined with the empirical formula of the liquid water content in air, a calculation method of the freezing fraction and collision coefficient was proposed using the ice surface temperature of transmission lines and the icing mass growth rate. The method to measure the icing mass growth rate based on online monitoring data and icing experiments was proposed. The icing experiments were carried out in the low-temperature ice chamber with the same parameters as the online monitoring environmental parameters. The values of the freezing fraction and collision coefficient were calculated by using the online monitoring data and icing experiments data respectively, the calculation results were compared. The maximum relative differences between the calculation results from the online monitoring data and the icing experiments data do not exceed 18%, which proves the validity of the calculation method of the freezing fraction and collision coefficient in ice accretion model of transmission lines.

Prediction of Transmission Line Icing Thickness Applying AMPSO-BP Neural Network Model

Prediction of Transmission Line Icing Thickness Applying AMPSO-BP Neural Network Model

Newly designed identifying method for ice thickness on high‐voltage transmission lines via machine vision

The icing of transmission lines will bring considerable challenges to the safe operation of the power grid. Therefore, a novel method combines machine vision and machine learning algorithms for identifying the ice thickness on high-voltage transmission line (HVTL) as proposed herein. First, noise and background interference in the image are filtered, and the grey image is used as input. Then, the algorithms of improved Canny edge detection, Hough transform, improved K-means clustering, and least-squares fitting are adopted in turn to locate the edges of conductors. Finally, according to the distance mapping model based on monocular vision, the ice thickness of the conductor is determined by calculating the width difference before and after icing. The experimental results show that the proposed method can accurately locate the edge of the conductor in both field and experimental environments. Moreover, it can ensure ideal effects under different illumination and hardly not be affected by distortion in both horizontal and vertical directions. Besides, the distance mapping model can map the pixel distance to the actual distance with high precision, no matter whether the background is simple or complex, and the calculated ice thickness has only a small deviation compared to the actual value. In addition, the proposed method shows high reliability and effectiveness when various interference such as different backgrounds, uneven icing, height difference changes, conductor movement, contrast changes, and conductor sag occur.

Research on Transmission Line Icing Tower Broken Fault Probability Calculation Model

Icing in winter is an important disaster that affects the normal operation of transmission lines. Among them, icing is easy to lead to tower collapse and disconnection. Due to long recovery time, great economic loss and social impact, it poses an important threat to the stable operation and reliable power supply of power grid. At present, the research on ice covered tower breaking fault of transmission lines mainly focuses on the finite element simulation analysis and calculation or exponential function fitting calculation. Due to the complexity of the mechanism of transmission line icing tower breakage, the current physical mechanism research conclusion cannot explain the occurrence of icing tower breakage. The existing analysis methods cannot accurately describe the characteristics of transmission line icing tower broken fault probability. Therefore, this paper proposes a probability calculation model based on probability distribution statistics, which can quantitatively calculate the failure probability of transmission line icing tower breakage. It has important theoretical significance and practical application value to provide scientific basis for transmission line to deal with icing disaster in advance.

Analysis on Galloping Properties from Transmission Line Vibration Data

At the beginning of 2020, a large-scale cold wave has affected the Bohai rim and central China, causing icing and galloping of transmission lines in Beijing, Tianjin, Hebei, Liaoning, Shandong, Hubei and other places, and causing some lines to trip. In this paper, the the temperature and other real-time data of line condition were considered, and a real-time analysis algorithm of galloping characteristics was proposed. In the process of this cold wave, a galloping phenomenon is recorded and analyzed in detail, which provided scientific and technological support for galloping prevention and control.

The Analysis of Meteorological Conditions for Icing on UHV Transmission Lines in Southeast Guizhou in 2018

Using ERA5 reanalysis data of ECMWF and the ultra-high voltage (UHV) transmission line icing observation system to analyze the meteorological conditions of conductor icing in Guizhou from January 24 to 29, 2018, Results show: The weather conditions for this icing event are that the temperature drops below 0°C, there is weak rainfall, and the humidity is generally between 95% and 100%; the direction is mainly northerly wind, with wind speed of 2~6m/s; the air pressure rises. The meteorological elements that are more relevant to icing are environmental temperature, air pressure, and precipitation. The relationship with wind speed and relative humidity is small.