| 张惊蛰,李海鹏,王燕,王博.基于5G通信的风力机叶片监测方法研究[J].电子测量与仪器学报,2026,40(4):289-298 |
| 基于5G通信的风力机叶片监测方法研究 |
| Research on monitoring method of wind turbine blades based on5G communication |
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| DOI： |
| 中文关键词: 风力机叶片 5G通信 监测系统 同步触发 动态调节 |
| 英文关键词:wind turbine blades 5G communication monitoring system synchronous trigger dynamic regulation |
| 基金项目:甘肃省重点研发计划项目（23YFGA0069）资助 |
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| Author | Institution |
| Zhang Jingzhe | School of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China |
| Li Haipeng | School of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China |
| Wang Yan | School of Green Energy and Storage, Lanzhou University of Technology, Lanzhou 730050, China |
| Wang Bo | School of Green Energy and Storage, Lanzhou University of Technology, Lanzhou 730050, China |
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| 摘要点击次数: 338 |
| 全文下载次数: 117 |
| 中文摘要: |
| 风力机叶片作为风力发电设备的核心部件,其健康状态对于整个设备的正常运行至关重要。针对运行状态下叶片状态参数实时监测需求,设计并实现了一套基于5G通信的分布式实时监测系统。系统采用双层分布式架构,由多节点数据采集单元集群和上位机软件构成。提出两级同步触发策略,实现全数据采集通道的高精度同步采样,触发延迟仅为最小采样周期的0.13%。融合压缩感知技术构建触发率和数据传输速率动态调节模型,并进行参数优化设计,实现了5G网络波动环境下触发率的自适应闭环调节以及数据采集与数据传输的动态匹配,提高了系统数据传输的可靠性。实验测试表明,系统本底噪声波动小于0.5%,非线性误差小于1.1%,通道一致性误差小于1%,压缩感知重构准确率达98%。该设计不仅为风力机叶片的实时监测提供有效的解决方案,其两级同步触发策略与动态调节机制也为其他在线实时监测系统开发提供技术参考。 |
| 英文摘要: |
| As the core component of wind power equipment, the health status of wind turbine blades is crucial for the normal operation of the entire system. In response to the real-time monitoring requirements of blade condition parameters during operation, a distributed real-time monitoring system based on 5G communication has been designed and implemented. The system employs a two-layer distributed architecture, consisting of a multi-node data acquisition unit cluster and upper computer software. A two-level synchronous trigger strategy has been proposed to achieve high-precision synchronous sampling across all data acquisition channels, with the trigger delay being only 0.13% of the minimum sampling period. A dynamic adjustment model of trigger rate and data transmission rate was developed by fusing the compressed sensing technology, and the parameters were optimized accordingly. This approach enables the dynamic matching of data collection and transmission in the variable environment of 5G networks, thereby improving the reliability of system data transmission. Experimental results indicate that the fluctuation of background noise is less than 0.5%, the nonlinear error is below 1.1%, the channel consistency error is under 1%, and the reconstruction accuracy of compressed sensing reaches 98%. This design not only provides an effective solution for the real-time monitoring of wind turbine blades but also serves as a technical reference for the development of other online real-time monitoring systems. |
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