Zhijie Zeng, Ye Tian, Dawei Chen, Xiaojian Wang, Rui Wang, Mingyuan Shi
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Firstly, this paper proposes a multi-layer attention network with a weather suppression function, which can effectively reduce or even eliminate the influence of weather conditions through the weather suppression module and improve the ability of the model to detect small targets through the multi-layer attention network. Based on the multi-layer attention network, this paper constructs the overall design scheme of the cloud-side intelligent inspection system of the wind farm UAV and integrates the multi-scene inspection and perception method based on the fusion of visual and point cloud information to achieve high-precision unmanned inspection. Furthermore, control experiments under normal and harsh conditions are designed to test the model, and the test results show that the model constructed in this paper has excellent performance. According to the comparison of relevant economic benefit indicators, the model in this paper takes into account both accuracy and complexity, and has good practical application value.</p>","PeriodicalId":55000,"journal":{"name":"IET Renewable Power Generation","volume":"19 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rpg2.70093","citationCount":"0","resultStr":"{\"title\":\"Multi-Layer Attention Network With Weather Suppression Function Unmanned Aerial Vehicle Fault Inspection of Wind Farm Stations\",\"authors\":\"Zhijie Zeng, Ye Tian, Dawei Chen, Xiaojian Wang, Rui Wang, Mingyuan Shi\",\"doi\":\"10.1049/rpg2.70093\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>With the continuous development of the wind power industry, the inspection work of wind farms is becoming more and more complex, and it is difficult for manual inspection to meet the actual needs. The application of unmanned aerial vehicle (UAV) intelligent inspection technology has brought a new inspection method to wind farms. However, drone inspection may be affected by many factors such as light conditions, rain, snow, fog and other weather factors at the specific site of the wind farm station. Therefore, this paper proposes a multi-layer attention neural network strategy for weather factor suppression of UAV fault inspection of wind farm stations to achieve high-precision unmanned inspection of wind farm stations. Firstly, this paper proposes a multi-layer attention network with a weather suppression function, which can effectively reduce or even eliminate the influence of weather conditions through the weather suppression module and improve the ability of the model to detect small targets through the multi-layer attention network. Based on the multi-layer attention network, this paper constructs the overall design scheme of the cloud-side intelligent inspection system of the wind farm UAV and integrates the multi-scene inspection and perception method based on the fusion of visual and point cloud information to achieve high-precision unmanned inspection. Furthermore, control experiments under normal and harsh conditions are designed to test the model, and the test results show that the model constructed in this paper has excellent performance. 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Multi-Layer Attention Network With Weather Suppression Function Unmanned Aerial Vehicle Fault Inspection of Wind Farm Stations
With the continuous development of the wind power industry, the inspection work of wind farms is becoming more and more complex, and it is difficult for manual inspection to meet the actual needs. The application of unmanned aerial vehicle (UAV) intelligent inspection technology has brought a new inspection method to wind farms. However, drone inspection may be affected by many factors such as light conditions, rain, snow, fog and other weather factors at the specific site of the wind farm station. Therefore, this paper proposes a multi-layer attention neural network strategy for weather factor suppression of UAV fault inspection of wind farm stations to achieve high-precision unmanned inspection of wind farm stations. Firstly, this paper proposes a multi-layer attention network with a weather suppression function, which can effectively reduce or even eliminate the influence of weather conditions through the weather suppression module and improve the ability of the model to detect small targets through the multi-layer attention network. Based on the multi-layer attention network, this paper constructs the overall design scheme of the cloud-side intelligent inspection system of the wind farm UAV and integrates the multi-scene inspection and perception method based on the fusion of visual and point cloud information to achieve high-precision unmanned inspection. Furthermore, control experiments under normal and harsh conditions are designed to test the model, and the test results show that the model constructed in this paper has excellent performance. According to the comparison of relevant economic benefit indicators, the model in this paper takes into account both accuracy and complexity, and has good practical application value.
期刊介绍:
IET Renewable Power Generation (RPG) brings together the topics of renewable energy technology, power generation and systems integration, with techno-economic issues. All renewable energy generation technologies are within the scope of the journal.
Specific technology areas covered by the journal include:
Wind power technology and systems
Photovoltaics
Solar thermal power generation
Geothermal energy
Fuel cells
Wave power
Marine current energy
Biomass conversion and power generation
What differentiates RPG from technology specific journals is a concern with power generation and how the characteristics of the different renewable sources affect electrical power conversion, including power electronic design, integration in to power systems, and techno-economic issues. Other technologies that have a direct role in sustainable power generation such as fuel cells and energy storage are also covered, as are system control approaches such as demand side management, which facilitate the integration of renewable sources into power systems, both large and small.
The journal provides a forum for the presentation of new research, development and applications of renewable power generation. Demonstrations and experimentally based research are particularly valued, and modelling studies should as far as possible be validated so as to give confidence that the models are representative of real-world behavior. Research that explores issues where the characteristics of the renewable energy source and their control impact on the power conversion is welcome. Papers covering the wider areas of power system control and operation, including scheduling and protection that are central to the challenge of renewable power integration are particularly encouraged.
The journal is technology focused covering design, demonstration, modelling and analysis, but papers covering techno-economic issues are also of interest. Papers presenting new modelling and theory are welcome but this must be relevant to real power systems and power generation. Most papers are expected to include significant novelty of approach or application that has general applicability, and where appropriate include experimental results. Critical reviews of relevant topics are also invited and these would be expected to be comprehensive and fully referenced.
Current Special Issue. Call for papers:
Power Quality and Protection in Renewable Energy Systems and Microgrids - https://digital-library.theiet.org/files/IET_RPG_CFP_PQPRESM.pdf
Energy and Rail/Road Transportation Integrated Development - https://digital-library.theiet.org/files/IET_RPG_CFP_ERTID.pdf