TDOA/DOA hybrid location method of partial discharge combined with blind signal separation algorithm

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.