电力设备外绝缘放电声-光协同检测及诊断技术

Acoustic-Optical Collaborative Detection and Diagnosis Technology for External Insulation Discharge of Power Equipment

本研究综合声成像技术和日盲紫外成像技术的各自优势,提出了一种针对电力设备外绝缘状态声-光协同可视化诊断方法。首先提出并开发了基于多重信号分类算法和单螺旋传感阵列的高聚焦精度电晕放电超声定位可视化方法;同时,在声成像有效视场内引入日盲紫外成像旁路。考虑环境噪声的对检测效果的影响,提出了一种可变权重的声-光协同可视化信息融合方法,通过声学强度点阵、紫外强度点阵和可见光像素视场的空间信息谱融合,实现了超声-紫外可视化诊断。在此基础上,进行了定位算法优化、视场配准及定位误差评估研究。测试表明,本研究提出声光协同可视化诊断的定位相对误差小于1%,并且对视场遮挡电晕放电的检出效果良好,提升了声成像或紫外成像单一方法的检出能力。

Based on the respective advantages of acoustic imaging technology and solar-blind ultraviolet imaging technology,an acousto-optic collaborative visual diagnosis method for the external insulation state of power equipment is proposed.First,based on multiple signal classification algorithm and single helical sensing array,a visualization method of corona discharge with high focusing accuracy is proposed and developed.At the same time,a solar-blind ultraviolet imaging bypass is introduced in the effective field of view(FOV) of acoustic imaging.Considering the influence of environmental noise on the detection effect,a variable-weight acousto-optic collaborative visualization information fusion method is proposed.Through the spatial information spectrum fusion of acoustic intensity lattice,ultraviolet intensity lattice and visible pixel FOV,ultrasound-ultraviolet visualization diagnosis is realized.On the basis of the above research,the author conducts research on the optimization of the positioning algorithm,the registration of the FOV and the evaluation of the positioning error.The test shows that the relative positioning error of proposed acousto-optic collaborative visualization diagnosis is less than 1%.The diagnosis method has a good effect on discharge detection with obstructed FOV,and improves the detection ability of acoustic imaging or ultraviolet imaging.