A Correlation Analysis-Based Hierarchical Identification Strategy for Hammerstein Models

Reliable mathematical models are essential for high-performance analysis and optimization of complex power and energy systems. However, inherent nonlinearities pose significant challenges to accurate model identification. The Hammerstein model, a typical block oriented nonlinear system, consists of a static nonlinear block followed by a linear dynamic block. This paper investigates the data-driven modeling method for the Hammerstein model and proposes a hierarchical identification strategy that integrates the correlation analysis with the Levenberg–Marquardt algorithm. Unlike traditional methods, this hierarchical algorithm strategy decouples the linear and nonlinear modules to avoid parameter coupling and reduces computational complexity. Simulations on a solid oxide fuel cell system and a real-world wind power system confirm the effectiveness and feasibility of the proposed method. The results demonstrate that the hierarchical identification strategy achieves accurate parameter estimation with satisfactory convergence performance.