Dynamic Simulation of Wind Turbine Power Generation Using Modified P&O-ANFIS Based MPPT Control

Abstract

This paper presents the modeling, simulation, and performance analysis of a smart Maximum Power Point Tracking (MPPT) control system integrated with a DC–DC converter for Wind Turbine (WT) power generation. Existing research remains limited in addressing hybrid MPPT controllers that merge conventional and intelligent techniques, are validated under varying wind and load conditions, and are integrated with wide-range high-voltage DC–DC converters with thorough dynamic performance assessment. The proposed intelligent MPPT scheme combines the conventional Perturb and Observe (P&O) algorithm with an Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance accuracy and dynamic response in extracting the maximum available power under varying wind and load conditions. The hybrid P&O–ANFIS-based controller adaptively adjusts the converter’s duty cycle to ensure optimal energy harvesting and efficient power transfer to the load. A boost converter is employed to step up the WT output voltage from 150 V to a range of 230–600 V.  Simulation results confirm that the modified P&O–ANFIS MPPT controller achieves superior tracking capability, exhibiting reduced steady-state oscillations and improved transient response compared to conventional MPPT methods. Moreover, the system demonstrates stable voltage regulation and reliable power continuity to the load. The overall average efficiency achieved by the proposed controller is approximately 90%, the average rise time, settling time and steady state time of the system are 0.023 s, 0.14 s, and 0.196 s, respectively. The findings emphasize the importance of adaptive intelligent control in optimizing and improving the overall performance of modern WT-based power generation system