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接地极缺陷单侧双位置磁声识别与量化

Defect identification and quantification for grounding electrode with single-sided dual-position electromagnetic ultrasonic excitation

  • 摘要: 输电杆塔接地极的缺陷会影响其正常的散流功能,威胁电力系统安全稳定运行,甚至人身安全。针对埋地接地网缺陷难以定位及智能识别的问题,以带放射线的接地扁钢为例,提出了一种免开挖或少开挖的接地扁钢单侧双位置激励超声兰姆(Lamb)波缺陷识别方法。首先,利用COMSOL软件构建换能器超声激励模型,仿真分析单侧激励电磁超声检测传播机理;然后,提出了一种单侧双位置缺陷识别方法,通过对比不同激励位置处的导波信号实现缺陷定位;在此基础上,通过特征提取-神经网络方法实现了对扁钢接地极缺陷的精确量化。结果表明,所提出的基于特征提取-神经网络的单侧激励换能器较商用双侧激励换能器在缺陷量化中具有更低的缺陷量化误差,即平均量化误差减小了约76.2%。

     

    Abstract: The normal current dissipation function can be affected by defects in the grounding electrode of the power transmission tower, impacting the safety of the power system and personal safety. Given that buried grounding grids are difficult to detect, this paper proposes a method for identifying grounding electrode defects in transmission towers using single-side dual-position excited ultrasonic Lamb waves. Geometric, ultrasonic excitation, and physical models are established, and the feasibility of ultrasonic guided wave detection is verified through simulation and experimentation. A single-sided dual-position defect identification method is proposed for on-site inspection, achieving defect localization by comparing guided wave signals at different excitation positions during actual inspections. Additionally, precise quantification of flat steel grounding electrode defects is achieved through feature extraction and neural network methods. Field test results show that, compared with commercial double-sided excitation transducers, the single-sided excitation transducer proposed in this paper has lower defect quantification error, reducing the average quantization error by approximately 76.2%.

     

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