(untitled) (International Conference on Energy, Intelligence Systems, and Cloud Computing (Ingenio 2026), 2026)
Aiming at the problems of significant non-stationary characteristics and complex strong noise interference in the electrical signals of the primary and secondary sides of transformers during actual operation, to achieve high precision, real-time and reliable online impedance monitoring, this paper proposes a joint signal processing and parameter identification scheme integrating Variational Mode Decomposition (VMD) and Variable Forgetting Factor Recursive Least Squares (VFF-RLS). First, VMD is used to adaptively decompose the noisy voltage and current signals at multiple scales, which effectively suppresses noise and accurately extracts pure fundamental components. In the signal preprocessing stage, the superiority of VMD in fundamental component extraction and anti-interference is verified through comparative analysis with the method combining wavelet threshold denoising and windowed interpolation. Subsequently, based on the classic T-type equivalent circuit model of the transformer, the VFF-RLS algorithm is adopted to complete the online identification of dynamic short-circuit impedance. Simulation results under three representative operating conditions and with SNRs of 40–60 dB show that the short-circuit impedance identification error can be maintained within 1%, demonstrating high accuracy and stability. This scheme provides an efficient and feasible technical support for the health assessment of transformer insulation and winding status, early fault warning, and safe operation and maintenance.