Application of adaptive gray wolf threshold denoising method in ultrasonic testing of transformer bushing leads
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摘要: 在对变压器套管引线进行超声检测时，检测精度易受噪声影响，难以准确判断套管端部内的引线状态，提出了一种自适应灰狼阈值去噪法。首先，通过小波变换对超声回波信号进行多层分解，采用基于包含多阶连续导数的梯度下降自适应阈值法，估计分层阈值；然后，结合灰狼算法，以最小梯度值为目标函数进行寻优，确定阈值大小并完成去噪。通过仿真与实例验证可得，采用文中算法去噪后，信号信噪比更高，均值误差更小，信号畸变程度较小，计算速度更快，可以有效保留超声回波信号起振位置等有用信息，更好地反映套管内引线的状态，具有一定的应用价值。Abstract: The ultrasonic detection accuracy of the transformer bushing is susceptible to noise, and it is difficult to accurately determine the state of the lead inside the bushing. This paper proposes an adaptive gray wolf threshold denoising method. Firstly, the ultrasonic echo signals are decomposed by multi-layer decomposition by wavelet transform, and the hierarchical threshold is estimated by the gradient descent adaptive threshold method based on multi-order continuous derivatives. Then, combined with the gray wolf algorithm, the minimum gradient value is used as the objective function to optimize, determine the threshold size and complete the denoising. Through simulation and case studies, it is found that the signal denoising ratio is higher and the mean signal error is smaller. At the same time, the smallest signal distortion and faster calculation speed can be obtained, and the useful information such as the vibration echo position of the ultrasonic echo signal can be effectively retained. The above-mentioned points can therefore improve the detection results and better reflect the state of the lead inside the casing, which shall have a certain application value.
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Keywords:
- ultrasonic detection /
- echo signal /
- denoising /
- adaptive threshold method /
- grey wolf optimization
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[1] 周安春,高理迎,冀肖彤,等.干式SF6气体绝缘换流变压器阀侧套管电流致热型放电故障分析及现场修复[J].电网技术,2018,42(5):1401-1409. [2] 肖天为. 变压器套管局部放电的特征与识别[J]. 高压电器,2015,51(7):141-147. [3] 董玉玺,解德英,曾力,等. 500 kV变压器套管的在线监测与现场高压介损测量研究[J]. 高压电器,2015,51(3):111-116. [4] 罗舜. 电力变压器套管将军帽发热故障的红外诊断分析[J]. 变压器,2018,55(1):50-53. [5] 林敏,黄劼,甘芳吉,等.基于特征点的在线超声波测厚系统性能诊断[J].无损检测,2019,41(5):56-60. [6] 王俊波,徐鑫,李国伟,等.高压开关柜局部放电声电联合定位检测技术[J].无损检测,2018,40(1):72-74,81. [7] ZHANG X P,DESAI M D.Adaptive denoising based on SURE risk[J]. IEEE Signal Processing Letters, 1998, 5(10):265-267.
[8] 李清泉,秦冰阳,司雯,等. 混合粒子群优化小波自适应阈值估计算法及用于局部放电去噪[J]. 高电压技术,2017,43(5):1485-1492. [9] 周建,向北平,倪磊,等.基于Shannon熵的自适应小波包阈值函数去噪算法研究[J].振动与冲击,2018,37(16):206-211,240. [10] MOHANTY S, SUBUDHI B, RAY P K. A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions[J]. IEEE Transactions on Sustainable Energy, 2016, 7(1):181-188.
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