风力发电场智能雷电预警系统

Hossein Foroozan, B. Franc, M. Vašak
{"title":"风力发电场智能雷电预警系统","authors":"Hossein Foroozan, B. Franc, M. Vašak","doi":"10.1109/FES57669.2023.10183023","DOIUrl":null,"url":null,"abstract":"Wind energy is one of the most important forms of renewable energy, and with the progress in this field, as the production capacity of wind turbines has increased, their height has also increased significantly. The height of wind turbines, number of them in a wind farm, and their specific location have increased the probability of lightning strikes and made them one of the most important hazards for wind turbines. Given the importance of maintenance and inspection for wind farms, creating a system for detecting safe time for these operations with low lightning probability is very useful. In this regard, by analyzing local meteorological data (pressure, temperature, wind speed, wind direction and humidity) and the lightning location system data an intelligent warning system for lightning hazard in a wind farm is developed based on machine learning methods. It is applied and tested on a case study of a wind farm in Croatia. The results show the success of this lightning hazard warning system in predicting the safe times with low lightning probability for the wind farm.","PeriodicalId":165790,"journal":{"name":"2023 International Conference on Future Energy Solutions (FES)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Intelligent Lightning Hazard Warning System for a Wind Farm\",\"authors\":\"Hossein Foroozan, B. Franc, M. Vašak\",\"doi\":\"10.1109/FES57669.2023.10183023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Wind energy is one of the most important forms of renewable energy, and with the progress in this field, as the production capacity of wind turbines has increased, their height has also increased significantly. The height of wind turbines, number of them in a wind farm, and their specific location have increased the probability of lightning strikes and made them one of the most important hazards for wind turbines. Given the importance of maintenance and inspection for wind farms, creating a system for detecting safe time for these operations with low lightning probability is very useful. In this regard, by analyzing local meteorological data (pressure, temperature, wind speed, wind direction and humidity) and the lightning location system data an intelligent warning system for lightning hazard in a wind farm is developed based on machine learning methods. It is applied and tested on a case study of a wind farm in Croatia. The results show the success of this lightning hazard warning system in predicting the safe times with low lightning probability for the wind farm.\",\"PeriodicalId\":165790,\"journal\":{\"name\":\"2023 International Conference on Future Energy Solutions (FES)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Future Energy Solutions (FES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FES57669.2023.10183023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Future Energy Solutions (FES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FES57669.2023.10183023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

风能是可再生能源中最重要的形式之一,随着这一领域的发展,随着风力发电机组生产能力的提高,其高度也有了明显的提高。风力涡轮机的高度、风力发电场的数量以及它们的特定位置都增加了雷击的可能性,使它们成为风力涡轮机最重要的危险之一。考虑到维护和检查风电场的重要性,创建一个系统来检测这些低雷击概率操作的安全时间是非常有用的。为此,通过对当地气象数据(气压、温度、风速、风向、湿度)和闪电定位系统数据的分析,开发了基于机器学习方法的风电场雷电灾害智能预警系统。该方法在克罗地亚一个风力发电场的案例研究中得到了应用和测试。结果表明,该雷害预警系统对风电场低雷击概率安全时间的预测是成功的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Lightning Hazard Warning System for a Wind Farm
Wind energy is one of the most important forms of renewable energy, and with the progress in this field, as the production capacity of wind turbines has increased, their height has also increased significantly. The height of wind turbines, number of them in a wind farm, and their specific location have increased the probability of lightning strikes and made them one of the most important hazards for wind turbines. Given the importance of maintenance and inspection for wind farms, creating a system for detecting safe time for these operations with low lightning probability is very useful. In this regard, by analyzing local meteorological data (pressure, temperature, wind speed, wind direction and humidity) and the lightning location system data an intelligent warning system for lightning hazard in a wind farm is developed based on machine learning methods. It is applied and tested on a case study of a wind farm in Croatia. The results show the success of this lightning hazard warning system in predicting the safe times with low lightning probability for the wind farm.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信