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针对电力设备局部放电(PD)超声波检测中存在的时间-空域特征解耦、硬件成本高及计算量大的技术瓶颈，提出基于核主成分分析(KPCA)伪白化的改进型非圆FastICA (mnc-FastICA)提取TDOA/DOA参数的混合定位方法。该方法通过时间-空域特征联合提取与智能优化机制，实现了小规模传感器阵列下的高精度定位。主要创新如下：首先，构建KPCA伪白化预处理框架，利用多项式核函数映射信号非线性升维再降维，在保留TDOA与DOA特征关联性的同时抑制环境噪声；其次，通过mnc-FastICA算法盲分离超声信号后，联合广义互相关法GCC与阵列流型解析技术同步提取TDOA/DOA参数；最后，建立融合双参数的最大似然估计模型，并引入非洲秃鹫优化算法实现全局最优解快速收敛。实验表明：在仅配置2个正交阵列（共8个传感器）的小规模硬件架构下，本方法TDOA估计误差降至2.34%，DOA估计精度优于2°，定位误差达1.54 cm。该方法有效解决了PD检测中时间-空域特征联合、硬件成本与定位精度的矛盾，为电力设备状态监测提供新方案.
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关键词:
- TDOA/DOA混合定位 /
- KPCA-mnc-FastICA /
- 局部放电 /
- 超声定位
To address the technical bottlenecks of spatiotemporal feature decoupling, high hardware costs, and excessive 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 using a small-scale sensor array. The key innovations are as follows: First, a KPCA-assisted pseudo-whitening preprocessing framework is constructed, leveraging 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 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 contradictions among spatiotemporal feature coupling, hardware cost, and localization accuracy in PD detection, offering a novel solution for condition monitoring of electrical equipment.-
Keywords:
- Hybrid TDOA/DOA /
- KPCA-mnc-FastICA /
- Partial discharge /
- Ultrasonic location
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