Spatial-Temporal Evolution Process and Characteristics of Thunderstorm Activity Based on Combinatorial Eight-Connectivity Tracking Algorithm
Huang Yijun1,2, Fan Yadong1,2, Wang Hongbin3, Cai Li1,2, Wang Jianguo1,2
1. Engineering Research Center of Lightning Protection & Grounding Technology Ministry of Education Wuhan 430072 China; 2. School of Electrical Engineering and Automation Wuhan University Wuhan 430072 China; 3. Guangzhou Electric Power Supply Bureau Guangzhou 510013 China
Abstract：Lightning is the main cause of damages to power systems. The planning of power system transmission corridors and line lightning protection are mainly based on the lightning density and the distribution of lightning days. Measures such as reducing the grounding resistance of towers and installing lightning arresters are taken to improve the insulation level of transmission lines. The traditional lightning density and the number of thunderstorm days are not able to present the temporal and spatial evolution characteristics of thunderstorm activities. Monitoring the movement of lightning activity can provide track and warning information for lightning protection of equipment. This paper presents a combinatorial eight-connectivity tracking method and evaluation indexes based on total lightning data, which is applied to analyze the temporal and spatial evolution of lightning activities in Guangzhou. The tracking method based on total lightning data is divided into two steps: cluster identification and cluster tracking. At the step of identification, the grid method is used to pre-process the total lightning events and the lightning region (112°E~115°E, 22°N~25°N) is gridded into 0.01°×0.01° grid boxes (longitude×latitude). Total lightning events are distributed into grid cells according to their geographical coordinates to form a total lightning density map in a single time interval. First, the electrically active region is identified by the 8-connectivity method and form primary lightning clusters. According to the size of the transmission line span, primary lightning clusters are merged into final lightning clusters within the distance of two grid cells. At the step of tracking, the algorithm takes influencing factors into account to match clusters between two successive time intervals, including history velocity, lightning cluster area and split-merge processes. In addition, the evaluation indexes contain the skill scores of the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) from the contingency table approach, the track characteristics of track duration, track linearity, track continuity and track complexity to quantitatively evaluate the performance of tracking method. The tracking method and evaluation are used to analyze the nine thunderstorms and case 1 are presented as an example. The case 1 has 313 tracks longer than 18 min, and tracks move in the northeast direction, with a mean speed of 51.4 km/h and a median speed of 43.0 km/h. The centroid of lightning clusters and temporal and spatial evolution characteristics of each track are counted. The evaluation indexes of POD, FAR and CSI are 63.3%, 41.4% and 43.8%, respectively, while the median track duration, mean jitter of track, incoherence and average number of splits and merges are 24 min, 2.85 km, 0.034 1 s-1 and 2.43, respectively. The following conclusions can be drawn from the nine thunderstorms based on the combinatorial eight-connectivity tracking method and evaluation indexes: (1) A total of 10,894 lightning clusters were identified in the nine lightning activities, with a median area of 22 km2. It is found that 1 490 lightning cluster tracks are longer than 18 min, of which simple tracks and complex tracks (involving split-ting or merging) account for half, respectively. (2) Six lightning activities move in the northeast direction, the rest move northwest, with a mean speed of 51.4 km/h and a median speed of 43.0 km/h. Simple tracks move slower than complex tracks. (3) POD, FAR and CSI are 65.6%, 39.2%, 46.4%, respectively, while the median track duration, mean jitter of track, incoherence and average number of splits and merges are 24 min, 2.80 km, 0.032 7 s-1 and 2.51, respectively. This algorithm can describe the temporal and spatial evolution of lightning activity and indexes are able to evaluate the tracking results, which provides an effective method for predicting thunderstorm movement and lightning warning of transmission lines.
黄怡鋆, 樊亚东, 王红斌, 蔡力, 王建国. 组合八邻域跟踪算法监测全闪电雷暴活动时空演变过程及特征[J]. 电工技术学报, 2024, 39(5): 1536-1547.
Huang Yijun, Fan Yadong, Wang Hongbin, Cai Li, Wang Jianguo. Spatial-Temporal Evolution Process and Characteristics of Thunderstorm Activity Based on Combinatorial Eight-Connectivity Tracking Algorithm. Transactions of China Electrotechnical Society, 2024, 39(5): 1536-1547.
[1] 马瑞阳, 郑栋, 姚雯, 等. 雷暴云特征数据集及我国雷暴活动特征[J]. 应用气象学报, 2021, 32(3): 358-369. Ma Ruiyang, Zheng Dong, Yao Wen, et al.Thunderstorm feature dataset and characteristics of thunderstorm activities in China[J]. Journal of Applied Meteorological Science, 2021, 32(3): 358-369. [2] 王建. 输电线路气象灾害风险分析与预警方法研究[D]. 重庆: 重庆大学, 2016. Wang Jian.Research on meteorological disaster risk analysis and fault early warning methods for overhead transmission lines[D]. Chongqing: Chongqing University, 2016. [3] 蔡国伟, 雷宇航, 葛维春, 等. 高寒地区风电机组雷电防护研究综述[J]. 电工技术学报, 2019, 34(22): 4804-4815. Cai Guowei, Lei Yuhang, Ge Weichun, et al.Review of research on lightning protection for wind turbines in alpine areas[J]. Transactions of China Electrotechnical Society, 2019, 34(22): 4804-4815. [4] 袁涛, 左思家, 司马文霞, 等. 紧凑型多腔室并联间隙雷电冲击闪络路径约束研究[J]. 电工技术学报, 2023, 38(11): 2989-2998. Yuan Tao, Zuo Sijia, Sima Wenxia, et al.Research on lightning flashover path constraints of compact multi-chamber parallel gap[J]. Transactions of China Electrotechnical Society, 2023, 38(11): 2989-2998. [5] Okabe S, Takami J.Occurrence probability of lightning failure rates at substations in consideration of lightning stroke current waveforms[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2011, 18(1): 221-231. [6] Perez E, Espinosa J, Aranguren D.On the development of dynamic stroke density for transmission line for power system operational applications[J]. International Journal of Electrical Power & Energy Systems, 2020, 116: 105527. [7] 袁涛, 王肖田, 司马文霞, 等山区输电线路雷击跳闸预警的融合算法研究[J]. 电工技术学报, 2023, 38(9): 2528-2540. Yuan Tao, Wang Xiaotian, Sima Wenxia,et al.Research on fusion algorithm of lightning strike trip warning for mountain transmission lines[J]. Transactions of China Electrotechnical Society, 2023, 38(9): 2528-2540. [8] Matsui M, Michishita K, Yokoyama S.Cloud-to-ground lightning flash density and the number of lightning flashes hitting wind turbines in Japan[J]. Electric Power Systems Research, 2020, 181: 106066. [9] Banjanin M S.Application possibilities of special lightning protection systems of overhead distribution and transmission lines[J]. International Journal of Electrical Power & Energy Systems, 2018, 100: 482-488. [10] 曾嵘, 周旋, 王泽众, 等. 国际防雷研究进展及前沿述评[J]. 高电压技术, 2015, 41(1): 1-13. Zeng Rong, Zhou Xuan, Wang Zezhong, et al.Review of research advances and fronts on international lightning and protection[J]. High Voltage Engineering, 2015, 41(1): 1-13. [11] 王东方, 孙竹玲, 袁善锋, 等. 北京多频段闪电三维定位网及一次雷暴过程的闪电时空演化特征[J]. 大气科学, 2020, 44(4): 851-864. Wang Dongfang, Sun Zhuling, Yuan Shanfeng, et al.Beijing broadband lightning network and the spatiotemporal evolution of lightning flashes during a thunderstorm[J]. Chinese Journal of Atmospheric Sciences, 2020, 44(4): 851-864. [12] 蔡力. 地基VLF/LF三维全闪电定位技术研究[D]. 武汉: 武汉大学, 2013. Cai Li.Ground-based VLF/LF three dimensional total lightning location technology[D]. Wuhan: Wuhan University, 2013. [13] 陈家宏, 张勤, 冯万兴, 等. 中国电网雷电定位系统与雷电监测网[J]. 高电压技术, 2008, 34(3): 425-431. Chen Jiahong, Zhang Qin, Feng Wanxing, et al.Lightning location system and lightning detection network of China power grid[J]. High Voltage Engineering, 2008, 34(3): 425-431. [14] Biagi C J, Cummins K L, Kehoe K E, et al.National Lightning Detection Network (NLDN) performance in southern Arizona, Texas, and Oklahoma in 2003-2004[J]. Journal of Geophysical Research, 2007, 112: D05208. [15] Shao X M, Stanley M, Regan A, et al.Total lightning observations with the new and improved Los Alamos Sferic Array (LASA)[J]. Journal of Atmospheric and Oceanic Technology, 2006, 23(10): 1273-1288. [16] Betz H D, Meneux B.LINET systems - 10 years experience[C]//2014 International Conference on Lightning Protection (ICLP), Shanghai, China, 2014: 1553-1557. [17] 高文胜, 张博文, 周瑞旭, 等. 基于雷电定位系统监测数据的雷暴云趋势预测[J]. 电网技术, 2015, 39(2): 523-529. Gao Wensheng, Zhang Bowen, Zhou Ruixu, et al.Nowcasting of the thunderstorm trend based on data collected by lightning location system[J]. Power System Technology, 2015, 39(2): 523-529. [18] 王羽, 向美辰, 邓冶强, 等. 基于时空聚类的引雷塔区域雷电地闪活动分析[J]. 高电压技术, 2021, 47(12): 4255-4264. Wang Yu, Xiang Meichen, Deng Yeqiang, et al.Analysis of lightning activity in area of triggering lightning tower based on spatial-temporal clustering[J]. High Voltage Engineering, 2021, 47(12): 4255-4264. [19] 司马文霞, 李永福, 杨庆, 等. 改进网格法及其在雷电参数统计中的应用[J]. 高电压技术, 2012, 38(8): 1834-1841. Sima Wenxia, Li Yongfu, Yang Qing, et al.Improved grid method and its application to statistics of lightning parameters[J]. High Voltage Engineering, 2012, 38(8): 1834-1841. [20] 王红斌, 程思, 范伟男, 等. 雷暴活动全闪电定位及空间演变过程分析[J]. 电工技术学报, 2021, 36(2): 373-379. Wang Hongbin, Cheng Si, Fan Weinan, et al.Total lightning location of thunderstorm activities and spatial evolution process analysis[J]. Transactions of China Electrotechnical Society, 2021, 36(2): 373-379. [21] Houston A L, Lock N A, Lahowetz J, et al.Thunderstorm observation by radar (ThOR): an algorithm to develop a climatology of thunderstorms[J]. Journal of Atmospheric and Oceanic Technology, 2015, 32(5): 961-981. [22] Dixon M, Wiener G.TITAN: thunderstorm identification, tracking, analysis, and nowcasting-a radar-based methodology[J]. Journal of Atmospheric and Oceanic Technology, 1993, 10(6): 785-797. [23] Kohn M, Galanti E, Price C, et al.Nowcasting thunderstorms in the Mediterranean region using lightning data[J]. Atmospheric Research, 2011, 100(4): 489-502. [24] Cai Li, Zou Xin, Wang Jianguo, et al.The Foshan total lightning location system in China and its initial operation results[J]. Atmosphere, 2019, 10(3): 149-153. [25] Huang Yijun, Fan Yadong, Cai L, et al. A new thunderstorm identification algorithm based on total lightning activity[J]. Earth and Space Science, 2022, 9(4): e2021EA002079. [26] He Lifeng, Ren Xiwei, Gao Qihang, et al.The connected-component labeling problem: a review of state-of-the-art algorithms[J]. Pattern Recognition, 2017, 70: 25-43. [27] del Moral A, Rigo T, Llasat M C. A radar-based centroid tracking algorithm for severe weather surveillance: identifying split/merge processes in convective systems[J]. Atmospheric Research, 2018, 213: 110-120. [28] Rigo T, Pineda N, Bech J.Analysis of warm season thunderstorms using an object-oriented tracking method based on radar and total lightning data[J]. Natural Hazards and Earth System Science, 2010, 10(119): 1881-1893. [29] 彭治班, 吴宝俊, 江剑民, 等. 提高小概率事件预报成功率的一条途径[J]. 气象, 2000, 26(2): 3-5, 10. Peng Zhiban, Wu Baojun, Jiang Jianmin, et al.A method to improve the forecast accuracy of rare events[J]. Meteorological Monthly, 2000, 26(2): 3-5, 10. [30] 张亚东. 基于深度学习和多维空间数据的闪电落区识别研究——以浙江宁波为例[D]. 南京: 南京信息工程大学, 2022.
2024, Vol. 39 
