High-performance sensorless finite-set model predictive control with parallel moving horizon estimation for real-time parameter adaptation in doubly fed induction generator-based wind energy

Abstract

While doubly fed induction generators (DFIGs) are critical to modern high-power wind energy conversion systems (WECSs), their nonlinear dynamics and high sensitivity to parameter drift present formidable control challenges. This paper proposes a high-performance, sensorless adaptive control framework for the rotor-side converter (RSC) utilizing a three-level neutral-point-clamped (3L-NPC) topology. The core innovation seamlessly integrates finite-set model predictive control (FS-MPC) with a parallel moving horizon estimation (P-MHE) scheme, driven by a compact, complex space-vector DFIG model mapped in the stationary frame. This novel formulation mathematically reduces the conventional fourth-order α – β model into a second-order representation. Crucially, it eliminates explicit dependence on the synchronous speed ( ω _s ), bypassing complex linear parameter-varying (LPV) structures to ensure high computational efficiency for real-time execution. The decoupled P-MHE architecture simultaneously delivers rapid state estimation and high-fidelity tracking of both rotor speed ( ω _m ) and critical ohmic resistances ( R _s , R _r ). These continuous parameter updates yield a self-correcting FS-MPC law that actively maintains optimal torque and current regulation despite severe parametric drift and measurement noise. Extensive MATLAB/Simulink validations confirm the strategy’s exceptional transient response, steady-state accuracy, and robust sensorless operation across a diverse spectrum of dynamic wind speeds and load conditions.