基于模糊综合评价法的输电线路山火遥感甄别算法

doi:10.11930/j.issn.1004-9649.201811108

中国电力 ›› 2019, Vol. 52 ›› Issue (11): 60-67.DOI: 10.11930/j.issn.1004-9649.201811108

基于模糊综合评价法的输电线路山火遥感甄别算法

唐波¹, 李耀伟¹, 叶莉², 黄力¹, 袁发庭¹, 陈昊¹, 奉彭¹

1. 三峡大学电气与新能源学院, 湖北宜昌 443002;
2. 国网湖北省电力有限公司武汉供电公司, 湖北武汉 430000

收稿日期:2018-11-26 修回日期:2019-06-20 出版日期:2019-11-05 发布日期:2019-11-05
通讯作者: 李耀伟(1995-),男,通信作者,硕士研究生,从事输电线路施工研究,E-mail:191906554@qq.com
作者简介:唐波(1978-),男,博士,教授,从事输变电工程电磁环境研究,E-mail:tangboemail@sina.com
基金资助:
电网环境保护国家重点实验室基金项目（GYW51201700590）。

Remote Sensing Discrimination Algorithm for Transmission Line Forest Fire Based on Fuzzy Comprehensive Evaluation Method

TANG Bo¹, LI Yaowei¹, YE Li², HUANG Li¹, YUAN Fating¹, CHEN Hao¹, FENG Peng¹

1. College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China;
2. State Grid Hubei Electric Power Co., Ltd. Wuhan Power Supply Company, Wuhan 430000, China

Received:2018-11-26 Revised:2019-06-20 Online:2019-11-05 Published:2019-11-05
Supported by:
This work is supported by Project of State Key Laboratory of Power Grid Environmental Protection (No.GYW51201700590).

摘要/Abstract

摘要： 传统输电线路山火遥感甄别算法通常采用亮温值阈值法直接对火点进行甄别，阈值难以准确设定会导致山火的误甄别或漏甄别问题。为实现输电线路山火遥感的准确甄别，在传统火点甄别算法基础上，提出了热点再甄别的思想，即依据亮温值阈值确定出可能的山火热点后，再对热点进行模糊综合评价以准确甄别山火火点。结合对广东省山火发生的时空规律进行研究，确定了导致山火发生的6个致灾因子，提出采用模糊综合评价法，实现输电线路山火的准确甄别。首先利用火险评判等级确定火险决策集及阈值，随后通过6个致灾因子建立因子集并作为输入，构建隶属度函数并设置权重向量，根据最大隶属度原则输出热点的置信度，并与阈值进行比较，判断火点的真伪。通过广东省2005—2016年12年间的山火数据库和该模型于2017年在广东电网的应用实例作为算例，对该模型进行验证。结果表明该模型较传统算法准确率高，可以满足输电线路山火的防治要求。

关键词: 输电线路, 山火甄别, 卫星遥感, 致灾因子, 模糊数学, 隶属度函数, 权重向量

Abstract: The traditional remote sensing discrimination algorithm for transmission line forest fires usually uses the brightness temperature threshold method to directly perform the fire point discrimination, which makes it difficult to accurately set the threshold value and frequently lead to false discrimination or omissions. In order to realize the accurate remote sensing discrimination of transmission line forest fires, the idea of second discrimination of hot spots is proposed based on the traditional fire point discrimination algorithm, that is, after determining the possible hotspots according to the threshold value of the brightness temperature, the fuzzy comprehensive evaluation is carried out for the hotspots to determine the exact point of fire. According to the research of the time and spatial distribution of forest fires in Guangdong Province, six hazard factors causing the occurrence of forest fires are determined, and a fuzzy comprehensive evaluation method is proposed to realize the accurate discrimination of transmission line forest fires. Firstly, the fire risk decision-making set and threshold are determined according to the fire risk evaluation levels. Then, the factor set is established as input with the six hazard factors, and the membership function is constructed and the weight vector is set. The confidence of the hotspot is output according to the principle of maximum membership degrees, and compared with the threshold to determine the authenticity of the fire point. The model is verified using the forest fire database in Guangdong Province from 2005 to 2016 and by a case study of Guangdong Power Grid in 2017. The results show that the model has higher accuracy than traditional algorithms and meets the prevention and control requirements for transmission line forest fires.

Key words: transmission lines, forest fire discrimination, satellite remote sensing, hazard factors, fuzzy mathematics, membership function, weight vector

中图分类号:

TM72

唐波, 李耀伟, 叶莉, 黄力, 袁发庭, 陈昊, 奉彭. 基于模糊综合评价法的输电线路山火遥感甄别算法[J]. 中国电力, 2019, 52(11): 60-67.

TANG Bo, LI Yaowei, YE Li, HUANG Li, YUAN Fating, CHEN Hao, FENG Peng. Remote Sensing Discrimination Algorithm for Transmission Line Forest Fire Based on Fuzzy Comprehensive Evaluation Method[J]. Electric Power, 2019, 52(11): 60-67.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201811108

https://www.electricpower.com.cn/CN/Y2019/V52/I11/60

参考文献

[1] 李家国, 顾行发, 余涛, 等. 澳大利亚东南部森林山火HJ卫星遥感监测[J]. 北京航空航天大学学报, 2010, 36(10):1221-1224 LI Jiaguo, GU Xingfa, YU Tao, et al. Detection of Australian southeast forest fire using HJ satellite[J]. Journal of Beijing University of Aeronautics and Astronautics, 2010, 36(10):1221-1224
[2] 江全元, 晏鸣宇. 基于概率统计的输电线路山火监测方法[J]. 高电压技术, 2015, 41(7):2302-2307 JIANG Quanyuan, YAN Mingyu. Improved method for forest fire spot detection near transmission line based on probability statistic[J]. High Voltage Engineering, 2015, 41(7):2302-2307
[3] FLASSE S P, CECCATO P. A contextual algorithm for AVHRR fire detection[J]. International Journal Remote Sensing, 1996, 17(2):419-424.
[4] 赵彬, 赵文吉, 潘军, 等. NOAA-AVHRR数据在吉林省东部林火信息提取中的应用[J]. 国土资源遥感, 2010, 22(1):77-80 ZHAO Bin, ZHAO Wenji, PAN Jun, et al. Application of NOAA-AVHRR data in forest fire information extraction in eastern Jilin Province[J]. Remote Sensing for Land & Resources, 2010, 22(1):77-80
[5] 叶立平, 陈锡阳, 何子兰, 等. 山火预警技术在输电线路的应用现状[J]. 电力系统保护与控制, 2014, 42(6):145-153 YE Liping, CHEN Xiyang, HE Zilan, et al. Present situation of forest fire early warning technology used for transmission line[J]. Power System Protection and Control, 2014, 42(6):145-153
[6] 张婕, 张文煜, 冯建东, 等. 基于亮温-植被指数-气溶胶光学厚度的MODIS火点监测算法研究[J]. 遥感技术与应用, 2016, 31(5):886-892 ZHANG Jie, ZHANG Wenyu, FENG Jiandong, et al. An improved algorithm for forest fire detection:a study based on brightness temperature vegetation index and AOD[J]. Remote Sensing Technology and Application, 2016, 31(5):886-892
[7] 李亚春, 张旭晖, 吴洪颜, 等. 一种增强的基于上下文火点遥感影像识别方法[J]. 中国图象图形学报, 2007, 12(9):1627-1632 LI Yachun, ZHANG Xuhui, WU Hongyan, et al. An enhanced contextual fire detection algorithm based on remote sensing images[J]. Journal of Image and Graphics, 2007, 12(9):1627-1632
[8] 郑伟, 邵佳丽, 王萌, 等. 多源卫星遥感草原火灾动态监测分析[J]. 自然灾害学报, 2013, 22(3):54-56 ZHENG Wei, SHAO Jiali, WANG Meng, et al. Dynamic monitoring and analysis of grassland fire based on multi-source satellite remote sensing data[J]. Journal of Natural Disasters, 2013, 22(3):54-56
[9] 许青云, 顾伟伟, 谢涛, 等. 秸秆焚烧火点遥感监测算法实现[J]. 遥感技术与应用, 2017, 32(4):728-733 XU Qingyun, GU Weiwei, XIE Tao, et al. Crop straw fire remote sensing monitoring and its algorithm implementation[J]. Remote Sensing Technology and Application, 2017, 32(4):728-733
[10] JUSTICE C O, KENDALL J D, DOWTY P R, et al. Satellite remote sensing of fires during the SAFARI campaign using NOAA-AVHRR data[J]. Journal of Geophysical Research, 1996, 101:23851-23863.
[11] PEREIRA J M C. A comparative evaluation of NOAA-AVHRR vegetation indexes for burned surface detection and mapping[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(1):217-226.
[12] 孙世军, 张治取, 韩洪. 疑似火点监测定位及电网运行预警方法[J]. 中国电力, 2017, 50(4):181-184 SUN Shijun, ZHANG Zhiqu, HAN Hong. Study on the method of power network operation early warning using remote sensor monitoring and locating of satellites based on power grid GIS[J]. Electric Power, 2017, 50(4):181-184
[13] WANG S D, MIAO L L, PENG G X. An improved algorithm for forest fire detection using HJ data[J]. Procedia Environmental Science, 2012, 13:140-150.
[14] LI Zhonghua, GUO Fanshao. The progress of operational forest fire monitoring with infrared remote sensing[J]. Journal of Forestry Research, 2017, 28(2):215-229.
[15] 森林火险气象等级:GB/T 36743-2018[S]. 2018.
[16] 陆佳政, 刘毓, 吴传平, 等. 输电线路山火卫星监测与告警算法研究[J]. 中国电机工程学报, 2015, 35(21):5511-5519 LU Jiazheng, LIU Yu, WU Chuanping, et al. Study on satellite monitoring and alarm calculation algorithm of wild fire near transmission lines[J]. Proceedings of the CSEE, 2015, 35(21):5511-5519
[17] LI Yonghua, YU Hongjie. Electric multiple unit bogie maintainability allocation with interval analysis and fuzzy evaluation method[J]. Journal of Donghua University(English Edition), 2014, 31(6):860-862.
[18] 王鑫. 基于层次分析法的继电保护专家系统研究[D]. 杭州:浙江大学, 2014. Wang Xin. Study on expert system of relay protection based on analytic hierarchy process[D]. Hangzhou:Zhejiang University, 2014

基于模糊综合评价法的输电线路山火遥感甄别算法

Remote Sensing Discrimination Algorithm for Transmission Line Forest Fire Based on Fuzzy Comprehensive Evaluation Method

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	金国锋, 杨世峰, 刘玲玲, 王凯, 王强, 王磊. 基于二维混沌映射正余弦算法的智能变电站虚回路自动连接技术[J]. 中国电力, 2024, 57(8): 152-158.
[2]	宋智伟, 黄新波, 纪超, 张凡, 张烨. 基于PCSA-YOLOv7 Former的输电线路连接金具及其锈蚀检测方法[J]. 中国电力, 2024, 57(6): 141-152.
[3]	王传琦, 伍历文, 邓志斌, 邓伟锋, 杨彬. 时间累积架空输电线路覆冰预测模型与算法综述[J]. 中国电力, 2024, 57(6): 153-164, 234.
[4]	叶煜明, 钱琪琪, 万正东, 张继钢. 基于嵌入法与集成学习的线路工程造价预测[J]. 中国电力, 2024, 57(5): 251-260.
[5]	罗美玲, 李紫肖, 郑涛, 于晓军, 吴建云, 谢海滨. 基于模糊多判据融合的单端暂态量保护新方案[J]. 中国电力, 2024, 57(3): 60-72.
[6]	李瀚儒, 刘智健, 来立永, 黄凌宇, 丁施尹, 刘任, 唐波. 考虑气象参数预测误差条件分布的架空输电线路载流量概率预测方法[J]. 中国电力, 2024, 57(2): 103-114.
[7]	杨鹏伟, 赵丽萍, 陈军法, 甄钊, 王飞, 李利明. 基于相似时段匹配与Transformer网络建模的分布式光伏超短期功率预测方法[J]. 中国电力, 2024, 57(12): 60-70.
[8]	张业茂, 李妮, 周翠娟, 鲁浩男. 750 kV高海拔交流输电线路无线电干扰测试数据分析[J]. 中国电力, 2023, 56(2): 77-85.
[9]	李永胜, 杨加伦, 郑维刚, 刘彬, 高正旭. 辽宁省特定重现期舞动区域分布图绘制[J]. 中国电力, 2022, 55(8): 129-134.
[10]	胡京, 邓颖, 蒋兴良, 曾蕴睿. 输电线路覆冰垭口微地形的特征提取与识别方法[J]. 中国电力, 2022, 55(8): 135-142.
[11]	马爱清, 王洁, 毕永翔. 500 kV交流线路对并行±800 kV直流线路带电作业人员安全防护的影响[J]. 中国电力, 2022, 55(6): 128-136.
[12]	方春华, 陶玉宁, 张威, 智李, 李景. 实测风速分析模拟及微地形下杆塔风速修正方法[J]. 中国电力, 2022, 55(6): 146-153.
[13]	徐鑫, 方春华, 智李, 丁璨, 董晓虎, 程绳, 孙维, 陶玉宁. 线激光清除架空线路树障时温度和效率分析[J]. 中国电力, 2022, 55(5): 94-101.
[14]	任华, 李健, 弥潇, 谷山强, 王剑, 吴敏, 曹伟, 张瑞. ±1 100 kV吉泉线雷击致灾因子权重分析及高风险杆塔筛选[J]. 中国电力, 2022, 55(2): 115-124.
[15]	毕洁廷. 计及山谷深度的输电线路雷电绕击率三维EGM研究[J]. 中国电力, 2021, 54(8): 91-97.

模态框（Modal）标题

基于模糊综合评价法的输电线路山火遥感甄别算法

Remote Sensing Discrimination Algorithm for Transmission Line Forest Fire Based on Fuzzy Comprehensive Evaluation Method

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics