Partial Discharge Multi-DoA Estimation Using Directional Array Received Signal Strength Information Based on TSDMNet

Extended exposure of power equipment to high voltages can lead to insulation failures. Accurate localization of partial discharge (PD) sources is essential for identifying these vulnerabilities effectively. Most deep learning methods are constrained to single-source scenarios or lack high localization resolution for multiple PD sources, leading to limited practical accuracy. To address these challenges, this paper proposes a novel deep learning-based method for Multi-DoA estimation of PD signals. Leveraging the theoretical foundation of the directional multiple signal classification (Dir-MUSIC) algorithm, the Temporal and Spatial Decomposition-Mixing Mechanism Network (TSDMNet) is introduced. This network captures temporal and spatial dependencies, enabling end-to-end processing of array-received data to identify the Multi-DoA of PD sources. Experimental results show that the proposed method outperforms classical deep learning models, particularly in low signal-to-noise ratio environments. For instance, at 0 dB, the root mean square error of direction estimation is less than 1 degree, demonstrating the model’s robustness and accuracy. Furthermore, the proposed TSDMNet eliminates the need for prior knowledge of source numbers or array geometry, enhancing its practical applicability.