Enabling Forecasting-Aided State Estimation in Active Distribution Networks via GLKAN-GRformer-D Pseudo-Measurement Modeling

Under high distributed generation (DG) penetration, state estimation of active distribution networks (ADNs) faces three core bottlenecks: measurement sparsity, system nonlinear coupling, and non-Gaussian noise interference, severely compromising grid operational situational awareness accuracy and reliability. To address these issues, this paper proposes a forecasting-aided state estimation (FASE) framework integrating Group-Rational enhanced Kolmogorov-Arnold Network (GLKAN) and Generalized Robust PCA Crossformer (GRformer) to synergistically optimize high-fidelity pseudo-measurement generation and accurate state estimation. First, to resolve the parameter redundancy and insufficient dynamic representation of traditional multi-layer perceptrons (MLPs), the GLKAN is proposed to reconstruct the feature extraction unit. Leveraging GLKAN's function decomposition capability and group-rational activation-based adaptability, this approach enables efficient multi-dimensional nonlinear dynamic characterization of ADNs while achieving effective parameter lightweighting. Second, to fundamentally suppress non-Gaussian noise interference on spatiotemporal feature modeling, the GRformer module is built. Its end-to-end low-rank valid component-noise residual separation significantly enhances pseudo-measurement anti-interference. Finally, GLKAN-GRformer is integrated with adaptive interpolation-based strong tracking extended Kalman filter (AISTEKF), which forms a full-link state estimation scheme covering feature extraction, noise suppression and state correction. Experiments on IEEE 33/118-bus distribution systems demonstrate that the proposed method outperforms traditional methods in estimation accuracy and non-Gaussian noise robustness, proving its technical superiority and engineering value in complex ADNs.