Robust Graph Diffusion for Multi-Task Learning in Ultra-High Voltage Direct Current Monitoring Systems

Abstract

In this work, we investigate the problem of multi-task learning (MTL) in ultra-high voltage direct current (UHVDC) monitoring systems. Considering the measurements are affected by wireless channel impairments, typically characterized by block fading and link noise. Such channel imperfections significantly degrade the performance of distributed estimation in real-world power system environments. Based on the graph signal processing method, we propose the multi-task robust decoupled diffusion least mean square algorithm (MT-RDDLMS). Specifically, a decoupled adapt-then-combine strategy is introduced to reduce the influence of wireless channels on data exchange among measurement units. Moreover, an average estimation method with an adaptive smoothing factor is developed to further suppress link noise and enhance estimation accuracy. Simulation results confirm the robustness and effectiveness of the proposed algorithm under realistic wireless channel conditions.

Keywords

graph signal processing / distributed multitask learning / ultra-high voltage direct current (UHVDC) systems / average estimation