1. 中国矿业大学电气工程学院,江苏省,徐州市,221116
2. 山东农业大学机械与电气工程学院,山东省,泰安市,271018
3. 国网天津市电力公司电力科学研究院, 天津市 西青区,300384
收稿:2026-03-20,
纸质出版:2026
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季洪鑫, 郑超, 汤子建, 等. 变压器内检机器人三维空间定位方法研究[J]. 中国电机工程学报, 2026,46(6):2601-2614.
JI Hongxin, ZHENG Chao, TANG Zijian, et al. Three-dimensional Spatial Positioning of Internal Inspection Robot for Transformers[J]. 2026, 46(6): 2601-2614.
季洪鑫, 郑超, 汤子建, 等. 变压器内检机器人三维空间定位方法研究[J]. 中国电机工程学报, 2026,46(6):2601-2614. DOI: 10.13334/j.0258-8013.pcsee.242528.
JI Hongxin, ZHENG Chao, TANG Zijian, et al. Three-dimensional Spatial Positioning of Internal Inspection Robot for Transformers[J]. 2026, 46(6): 2601-2614. DOI: 10.13334/j.0258-8013.pcsee.242528.
针对大型油浸式变压器内部巡检机器人在变压器金属封闭结构内因强噪声干扰和常规方法分辨率不足导致的三维空间精准定位难题,该文提出一种基于改进的灰狼优化算法-变分模态分解(grey wolf optimization-variational mode decomposition,GWO-VMD)去噪和AR模型改进的最小方差无偏估计(autoregressive model-minimum variance distortionless response,AR-MVDR)波束形成算法的三维空间超声定位方法。首先,为抑制噪声,通过改进的GWO-VMD方法对变压器机器人发出的超声信号进行分解,并自适应地搜寻各个模态的最佳中心频率和带宽,提取定位信号频带的模态,抑制噪声频带的模态,获得高信噪比的信号;其次,为提高方位分辨率,采用一种AR模型改进的MVDR波束形成算法,通过在MVDR的权向量中引入增强因子来提高该方法的方位分辨率。结果表明,提出的GWO-VMD去噪相比与传统EMD去噪,超声定位信号的信噪比从4.276 3提升至7.901 2 dB,波形相似度从81.27%提升至92.48%,均方根误差从0.100 9降低至0.060 9。在1 200 mm×1 000 mm×1 100 mm的变压器试验箱中,用GWO- VMD方法对接收信号去噪后,采用AR-MVDR定位方法,得到最终定位结果为(490 mm
392 mm
394 mm)。三维空间相对定位误差在3%以内,最大定位误差小于20 mm,满足工程定位要求。
When inspecting large oil-immersed transformers
the inspection robot faces the severe challenge of positioning itself inside the transformer due to the transformer’s large volume and metal enclosed structure. To address this issue
this paper proposes a three-dimensional ultrasound localization method. This method is based on a Grey Wolf Optimization-Variational Mode Decomposition (GWO- VMD) denoising method and a Minimum Variance Distortionless Response (MVDR) beamforming algorithm improved by an AutoRegressive Model (AR) model. This paper addresses the strong on-site noise interference by decomposing the ultrasonic signals emitted by the transformer robot using an improved GWO-VMD method. This method adaptively searches for the optimal center frequency and bandwidth of each mode. By extracting the mode of the positioning signal frequency band and suppressing the mode of the noise frequency band
the autonomously selected excellent Intrinsic Mode Function (IMF) is reconstructed to obtain a high signal-to-noise ratio signal. This paper adopts an AR model-improved MVDR beamforming algorithm to address the low spatial positioning resolution of the inspection robot using conventional beamforming methods. The proposed method uses a nine-element L-shaped sensor array to accurately position the internal inspection robot inside the transformer. Simulation and experimental results demonstrate that compared with traditional EMD denoising methods
the proposed GWO-VMD shows significantly superior denoising performance. The signal-to-noise ratio of the ultrasonic positioning signal increases from 4.2763 to 7.9012 dB
the wave shape similarity increases from 81.27% to 92.48%
and the root mean square error reduces from 0.1009 to 0.0609. In a transformer test box with three-dimensional sizes of 1 200 mm×1 000 mm×1 100 mm (length×width×height)
the GWO-VMD denoising algorithm and the AR-MVDR positioning method are sequentially used to achieve millimeter-level positioning
with the final spatial positioning coordinates of 490
392
394 mm. The relative positioning error of the inspection robot inside the transformer is less than 3%
and the maximum positioning error of less than 20 mm
meeting the engineering positioning requirements.
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