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To address the technical bottleneck of decoupling spatiotemporal feature, high hardware costs, and high computational complexity in ultrasonic detection of partial discharge (PD) in electrical equipment, this paper proposes a TDOA/DOA hybrid localization method based on kernel principal component analysis (KPCA) and modified noncircular FastICA (mnc-FastICA). By integrating spatiotemporal feature extraction with intelligent optimization mechanisms, this method achieves high-precision localization by using a small-scale sensor array. The key innovations are as follows. First, a KPCA-assisted pseudo-whitening preprocessing framework is constructed by using polynomial kernel mapping for nonlinear signal dimensionality reduction, which preserves the correlation between time delay (TDOA) and direction-of-arrival (DOA) features while suppressing environmental noise. Second, after the blind separation of ultrasonic signals via the mnc-FastICA algorithm, TDOA/DOA parameters are synchronously extracted through a combination of the generalized cross-correlation (GCC) method and array manifold analysis. Finally, a maximum likelihood estimation model integrating dual parameters is established, and the African vulture optimization algorithm (AVOA) is introduced to accelerate global optimal solution convergence. Experimental results demonstrate that with a compact hardware configuration of two orthogonal arrays (8 sensors in total), the proposed method achieves a TDOA estimation error of 2.34%, DOA estimation accuracy better than 2°, and localization errors as low as 1.54 cm. This approach effectively resolves the discrepancies among spatiotemporal feature coupling, hardware cost, and localization accuracy in PD detection, providing a novel solution for condition monitoring of electrical equipment.
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Keywords:
- hybrid TDOA/DOA /
- KPCA-mnc-FastICA /
- partial discharge /
- ultrasonic location
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表 1 PD源和3路传感器阵列坐标
Table 1. Coordinates of Needle-plate model and three sensor array.
设备类型 坐标/cm PD源 (20, 10, 10) 传感器阵列1 (0, 30, 15) 传感器阵列2 (40, 0, 5) 传感器阵列3 (60, 20, 20) 表 2 TDOA估计结果
Table 2. Results of TDOA estimation of the test.
时间差 理论时间差/ms 试验时间差/ms 误差/μs t21 0.1568 0.1530 –3.8 t31 0.2275 0.2221 –5.4 表 3 DOA估计结果
Table 3. Results of DOA estimation of the test.
阵列 理论方位角/( º) 试验方位角/( º) 方位角误差/( º) 理论俯仰角/( º) 试验俯仰角/( º) 俯仰角误差/( º) 1 –45 –44 1 –10 –10 0 2 153 155 2 12 11 1 3 –165 –166 1 –13 –14 1 表 4 局放定位结果
Table 4. Localization results of PD source.
阵列组合 阵列序号 估计坐标/cm 均方根误差/cm 1 1和2 (20.95, 11.21, 10.09) 1.54 2 1和3 (20.55, 11.79, 9.89) 1.88 3 2和3 (21.25, 12.11, 9.95) 2.45 4 1, 2和3 (21.13, 10.12, 10.14) 1.14 -
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