{"title":"基于 SBAS-InSAR 技术的陆基风力/太阳能电站场址变形监测研究","authors":"Junke Guo, Ling Liu, Yongfeng Zheng, Wei Cai, Zhijun Wang, Shangqi Wang","doi":"10.4108/ew.5656","DOIUrl":null,"url":null,"abstract":"INTRODUCTION: In recent years, China has been building extensive wind/solar power stations. During the construction and operation of land-based wind/solar power stations, deformation monitoring is an important method to investigate the station stability. \nOBJECTIVES: Therefore, this study uses Sentinel-1 data and time-series InSAR technology to monitor the deformation of photovoltaic and wind power stations in Qingyuan County. \nMETHODS: InSAR technology obtains deformation rate maps in the radar line of sight (LOS) direction for a wide area around the power station sites. Since wind/solar power stations are mainly located in natural environments with relatively dense vegetation coverage, this paper proposes a SBAS-InSAR method integrated with spatiotemporal filtering to accurately extract the time series deformation over a large area. Based on the statistical characteristic difference between the deformation and the atmospheric delay, spatiotemporal filterings are applied to remove the atmospheric delay from the InSAR derived deformation results. \nRESULTS: The experimental results show that spatiotemporal filtering is an effective and fast method to remove atmospheric delay. \nCONCLUSION: The integration of BSAS-InSAR with spatiotemporal filtering has great potential applications in the deformation monitoring of land-based wind/solar power station sites, which is critical for the construction and operation of land-based wind/solar power stations.","PeriodicalId":53458,"journal":{"name":"EAI Endorsed Transactions on Energy Web","volume":"36 6","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Land-Based Wind/Solar Power Station Site Deformation Monitoring Based on SBAS-InSAR Technology\",\"authors\":\"Junke Guo, Ling Liu, Yongfeng Zheng, Wei Cai, Zhijun Wang, Shangqi Wang\",\"doi\":\"10.4108/ew.5656\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"INTRODUCTION: In recent years, China has been building extensive wind/solar power stations. During the construction and operation of land-based wind/solar power stations, deformation monitoring is an important method to investigate the station stability. \\nOBJECTIVES: Therefore, this study uses Sentinel-1 data and time-series InSAR technology to monitor the deformation of photovoltaic and wind power stations in Qingyuan County. \\nMETHODS: InSAR technology obtains deformation rate maps in the radar line of sight (LOS) direction for a wide area around the power station sites. Since wind/solar power stations are mainly located in natural environments with relatively dense vegetation coverage, this paper proposes a SBAS-InSAR method integrated with spatiotemporal filtering to accurately extract the time series deformation over a large area. Based on the statistical characteristic difference between the deformation and the atmospheric delay, spatiotemporal filterings are applied to remove the atmospheric delay from the InSAR derived deformation results. \\nRESULTS: The experimental results show that spatiotemporal filtering is an effective and fast method to remove atmospheric delay. \\nCONCLUSION: The integration of BSAS-InSAR with spatiotemporal filtering has great potential applications in the deformation monitoring of land-based wind/solar power station sites, which is critical for the construction and operation of land-based wind/solar power stations.\",\"PeriodicalId\":53458,\"journal\":{\"name\":\"EAI Endorsed Transactions on Energy Web\",\"volume\":\"36 6\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EAI Endorsed Transactions on Energy Web\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4108/ew.5656\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EAI Endorsed Transactions on Energy Web","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4108/ew.5656","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
Research on Land-Based Wind/Solar Power Station Site Deformation Monitoring Based on SBAS-InSAR Technology
INTRODUCTION: In recent years, China has been building extensive wind/solar power stations. During the construction and operation of land-based wind/solar power stations, deformation monitoring is an important method to investigate the station stability.
OBJECTIVES: Therefore, this study uses Sentinel-1 data and time-series InSAR technology to monitor the deformation of photovoltaic and wind power stations in Qingyuan County.
METHODS: InSAR technology obtains deformation rate maps in the radar line of sight (LOS) direction for a wide area around the power station sites. Since wind/solar power stations are mainly located in natural environments with relatively dense vegetation coverage, this paper proposes a SBAS-InSAR method integrated with spatiotemporal filtering to accurately extract the time series deformation over a large area. Based on the statistical characteristic difference between the deformation and the atmospheric delay, spatiotemporal filterings are applied to remove the atmospheric delay from the InSAR derived deformation results.
RESULTS: The experimental results show that spatiotemporal filtering is an effective and fast method to remove atmospheric delay.
CONCLUSION: The integration of BSAS-InSAR with spatiotemporal filtering has great potential applications in the deformation monitoring of land-based wind/solar power station sites, which is critical for the construction and operation of land-based wind/solar power stations.
期刊介绍:
With ICT pervading everyday objects and infrastructures, the ‘Future Internet’ is envisioned to undergo a radical transformation from how we know it today (a mere communication highway) into a vast hybrid network seamlessly integrating knowledge, people and machines into techno-social ecosystems whose behaviour transcends the boundaries of today’s engineering science. As the internet of things continues to grow, billions and trillions of data bytes need to be moved, stored and shared. The energy thus consumed and the climate impact of data centers are increasing dramatically, thereby becoming significant contributors to global warming and climate change. As reported recently, the combined electricity consumption of the world’s data centers has already exceeded that of some of the world''s top ten economies. In the ensuing process of integrating traditional and renewable energy, monitoring and managing various energy sources, and processing and transferring technological information through various channels, IT will undoubtedly play an ever-increasing and central role. Several technologies are currently racing to production to meet this challenge, from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems – which we generically term the ‘energy web’ – calling for major paradigm shifts highly disruptive of the ways the energy sector functions today. The EAI Transactions on Energy Web are positioned at the forefront of these efforts and provide a forum for the most forward-looking, state-of-the-art research bringing together the cross section of IT and Energy communities. The journal will publish original works reporting on prominent advances that challenge traditional thinking.