Based on comprehensive observations of 20 wire icing events during winter from 2019 to 2021,we investigated the characteristics of the icing properties,the atmospheric boundary layer structure,the raindrop size distri...Based on comprehensive observations of 20 wire icing events during winter from 2019 to 2021,we investigated the characteristics of the icing properties,the atmospheric boundary layer structure,the raindrop size distribution,and their associated effects on the ice accretion mechanism in the mountainous region of Southwest China.The maximum ice weight was positively correlated with the duration of ice accretion in the mountainous area.The duration of precipitation accounted for less than 20%of the icing period in the mountainous area,with solid-phase hydrometeors being predominant.Icing events,dominated by freezing rain(FR)and mixed rain–graupel(more than 70%),were characterized by glaze or highdensity mixed icing.The relationship between the melting energy and refreezing energy reflected the distribution characteristics of the proportion of FR under mixed-phase precipitation.The intensity of the warm layer and the dominant precipitation phase significantly affected the variations in the microphysical properties of FR.The melting of large dry snowflakes significantly contributed to FR in the mountainous areas,resulting in smaller generalized intercepts and larger mass-weighted mean diameters in the presence of a stronger warm layer.Under a weaker warm layer,the value of the massweighted mean diameter was significantly smaller because of the inability of large solid particles to melt.Finally,FR in the mountainous area dominated the ice weight during the rapid ice accumulation period.A numerical simulation of FR icing on wires effectively revealed the evolution of disaster-causing icing in mountainous areas.展开更多
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 disa...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 scarce raw data by the Wasserstein Generative Adversarial Network with Gradient Penalty(WGAN-GP)technique,records historical environmental information by a recurrent neural network,and evaluates the ice warning levels by a classifier.At each time point,the model diagnoses whether the current sensor failure is due to icing or strong winds.If it is determined that the wire is covered with ice,the icing thickness will be calculated after the wind-induced tension is removed from the ice-wind coupling tension.Our new model was evaluated using data from the power grid in an area with extreme weather.The results show that the proposed model has significant improvements in accuracy compared with traditional models.展开更多
To investigate the mechanism of wire icing growth and simulate the icing growth rate,this study analyzed two cases of wire icing observed in Lushan(Jiangxi Province,South China)during winter 2016 under conditions of c...To investigate the mechanism of wire icing growth and simulate the icing growth rate,this study analyzed two cases of wire icing observed in Lushan(Jiangxi Province,South China)during winter 2016 under conditions of coexisting freezing rain and supercooled fog.By combining meteorological elements with the microphysical parameters of coexisting freezing rain and supercooled fog,the correlation between the icing growth rate and each of these factors was examined.To simulate the icing growth process,this study adopted four widely used machine learning models:random forest(RF),support vector machine(SVM),convolutional neural network(CNN),and extreme learning machine(ELM)models.For both studied cases,the results indicated that the rain rate,temperature,wind speed(≥1 m s^(-1)),and wind direction exhibited statistically significant positive correlation with the icing growth rate.In the stronger wind scenario(Case 1),the wire icing growth rate was negatively correlated with the number concentration,liquid water content,mean diameter,and mean volumetric diameter of the precipitation particles,with wind speed being the most important factor in the RF model.The icing growth was driven primarily by the increased rain rate,which subsequently led to higher liquid water content.In the weaker wind scenario(Case 2),the wire icing growth rate exhibited positive correlation with the number concentration,liquid water content,and mean diameter of the precipitation particles,with the mean volumetric diameter of supercooled fog droplets being the most important factor in the RF model.The icing growth was attributable primarily to increase in the number of water-phase particles and overall increase in particle size,leading to increase in liquid water content.All four machine learning models successfully simulated the icing growth process,yielding results that outperformed those derived from traditional empirical formulas and numerical simulations,with the RF,SVM,and CNN models demonstrating particularly strong performance.展开更多
基金funded by the National Natural Science Foundation of China(Grant No.42325503)the Hubei Provincial Natural Science Foundation and the Meteorological Innovation and Development Project of China(Grant Nos.2023AFD096 and 2022CFD122)+1 种基金the Natural Science Foundation of Wuhan(Grant No.2024020901030454)the Beijige Foundation of NJIAS(Grant No.BJG202304)。
文摘Based on comprehensive observations of 20 wire icing events during winter from 2019 to 2021,we investigated the characteristics of the icing properties,the atmospheric boundary layer structure,the raindrop size distribution,and their associated effects on the ice accretion mechanism in the mountainous region of Southwest China.The maximum ice weight was positively correlated with the duration of ice accretion in the mountainous area.The duration of precipitation accounted for less than 20%of the icing period in the mountainous area,with solid-phase hydrometeors being predominant.Icing events,dominated by freezing rain(FR)and mixed rain–graupel(more than 70%),were characterized by glaze or highdensity mixed icing.The relationship between the melting energy and refreezing energy reflected the distribution characteristics of the proportion of FR under mixed-phase precipitation.The intensity of the warm layer and the dominant precipitation phase significantly affected the variations in the microphysical properties of FR.The melting of large dry snowflakes significantly contributed to FR in the mountainous areas,resulting in smaller generalized intercepts and larger mass-weighted mean diameters in the presence of a stronger warm layer.Under a weaker warm layer,the value of the massweighted mean diameter was significantly smaller because of the inability of large solid particles to melt.Finally,FR in the mountainous area dominated the ice weight during the rapid ice accumulation period.A numerical simulation of FR icing on wires effectively revealed the evolution of disaster-causing icing in mountainous areas.
基金supported by the Science and Technology Project of State Grid Corporation of China(SGXJDK00GYJS2400035).
文摘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 scarce raw data by the Wasserstein Generative Adversarial Network with Gradient Penalty(WGAN-GP)technique,records historical environmental information by a recurrent neural network,and evaluates the ice warning levels by a classifier.At each time point,the model diagnoses whether the current sensor failure is due to icing or strong winds.If it is determined that the wire is covered with ice,the icing thickness will be calculated after the wind-induced tension is removed from the ice-wind coupling tension.Our new model was evaluated using data from the power grid in an area with extreme weather.The results show that the proposed model has significant improvements in accuracy compared with traditional models.
基金Supported by the National Natural Science Foundation of China(42075063 and 42075066)Jiangsu Graduate Scientific Research Innovation Project(KYCX23_1316)+2 种基金China Scholarship Council(CSC)(202309040027)Meteorological Observation Centre of the China Meteorological Administration Field Experimental Project in 2024(GCSYJH24-30)Project of China Meteorological Administration Training Centre(2025CMATCPY08)。
文摘To investigate the mechanism of wire icing growth and simulate the icing growth rate,this study analyzed two cases of wire icing observed in Lushan(Jiangxi Province,South China)during winter 2016 under conditions of coexisting freezing rain and supercooled fog.By combining meteorological elements with the microphysical parameters of coexisting freezing rain and supercooled fog,the correlation between the icing growth rate and each of these factors was examined.To simulate the icing growth process,this study adopted four widely used machine learning models:random forest(RF),support vector machine(SVM),convolutional neural network(CNN),and extreme learning machine(ELM)models.For both studied cases,the results indicated that the rain rate,temperature,wind speed(≥1 m s^(-1)),and wind direction exhibited statistically significant positive correlation with the icing growth rate.In the stronger wind scenario(Case 1),the wire icing growth rate was negatively correlated with the number concentration,liquid water content,mean diameter,and mean volumetric diameter of the precipitation particles,with wind speed being the most important factor in the RF model.The icing growth was driven primarily by the increased rain rate,which subsequently led to higher liquid water content.In the weaker wind scenario(Case 2),the wire icing growth rate exhibited positive correlation with the number concentration,liquid water content,and mean diameter of the precipitation particles,with the mean volumetric diameter of supercooled fog droplets being the most important factor in the RF model.The icing growth was attributable primarily to increase in the number of water-phase particles and overall increase in particle size,leading to increase in liquid water content.All four machine learning models successfully simulated the icing growth process,yielding results that outperformed those derived from traditional empirical formulas and numerical simulations,with the RF,SVM,and CNN models demonstrating particularly strong performance.
