Kalman Filter Prediction Method for Tracking the Fuzzy-Based Maximum Power Point in Wind Turbine Generators

The wind energy sector has grown significantly over the past few decades and is expected to continue this trend as power electronic converters advance. This paper presents a novel control approach for wind turbine systems that combines Kalman Filter prediction with fuzzy logic-based maximum power point tracking (MPPT). The method addresses the challenge of optimizing power generation amid variable wind conditions by proactively adjusting turbine rotational speed and pitch angle. The system uses a Kalman Filter to predict wind speed changes and a fuzzy logic controller to optimize MPPT performance. The model consists of a wind power generation model, power electronic converters, and an MPPT controller. Simulation results in MATLAB demonstrate that compared to traditional vector control and proportional integral methods, the proposed approach achieves superior performance with: reduced delay time (4.93 seconds vs 5.6 seconds), faster rise time (0.06 seconds vs 3.5 seconds), quicker settling time (0.1 seconds vs 26 seconds), zero overshoot (compared to 2-12.46%), and elimination of permanent error. These improvements enable more efficient and stable wind energy generation under variable conditions.