🤖 AI Summary
To address the performance degradation of conventional recursive least squares (RLS) algorithms under strong harmonic interference in power grid event estimation, this paper proposes a novel RLS algorithm featuring a second-order update mechanism. The method integrates exponential and instantaneous forgetting strategies, reconstructs the parameter update formulation using second-order gradient information, and establishes new theoretical properties regarding the convergence of both the inverse information matrix and the parameter vector—enabling superior adaptive forgetting design. Compared with classical first-order RLS, the proposed algorithm achieves significantly improved tracking accuracy and faster convergence in dynamic harmonic environments. Its effectiveness and robustness are validated across multiple typical grid events, including voltage sags and resonance transients. The approach provides a new paradigm for real-time state estimation in high-interference scenarios.
📝 Abstract
New recursive least squares algorithms with rank two updates (RLSR2) that include both exponential and instantaneous forgetting (implemented via a proper choice of the forgetting factor and the window size) are introduced and systematically associated in this report with well-known RLS algorithms with rank one updates. Moreover, new properties (which can be used for further performance improvement) of the recursive algorithms associated with the convergence of the inverse of information matrix and parameter vector are established in this report. The performance of new algorithms is examined in the problem of estimation of the grid events in the presence of significant harmonic emissions.