Dynamic evolving neuro-fuzzy inference system-based maximum power tracking controller for variable speed WECS

Abstract

Reliable MPPT models are essential for maximizing energy conversion efficiency in wind energy conversion systems operating under highly dynamic atmospheric conditions. While most artificial intelligence (AI) controllers such as ANN and ANFIS provide robust control surfaces, their real-time deployment is frequently constrained by a reliance on static parameters, a lack of online adaptability, or severe computational burdens. To address these limitations, this paper presents an intelligent, self-organizing MPPT controller utilizing a DENFIS for a variable-speed PMSG wind turbine framework. By leveraging the Evolving Clustering Method, the proposed DENFIS-MPPT controller dynamically instantiates, updates, and discards localized fuzzy rules online, enabling continuous adaptive learning and rapid convergence without requiring extensive offline training or pre-calculated datasets. The computational and transient effectiveness of the proposed architecture is validated using 3 kW wind turbine model simulated within the MATLAB/Simulink environment. System performance is systematically evaluated under stochastic and gust wind profiles, with execution and event-driven characteristics benchmarked via the Simulink and Solver Profilers against ANN and ANFIS configurations. The empirical results demonstrate that DENFIS achieves superior maximum power extraction over a wide operational envelope. Detailed computational profiling reveals that DENFIS yields an optimized average execution cost of (24.92 (Formula presented) per iteration compared to 130.00 (Formula presented) for ANFIS, achieving an 80.8% reduction in structural computational overhead while incurring only a marginal execution premium over the rigid ANN baseline (14.11 (Formula presented) ). In terms of transient performance, DENFIS successfully stabilizes electrical variations, restricting output active power overshoot to a minimized 5.47%, thereby outperforming both ANFIS (9.00%) and the baseline ANN. These findings demonstrate that DENFIS provides an optimal architectural compromise between micro-level processing efficiency and macro-level adaptive intelligence, offering a highly viable, scalable control paradigm for next-generation real-time WECS applications.