A multi dataset validation model for hybrid feature selection in wind energy maximum power point tracking systems.

Maximum power point tracking (MPPT) in wind turbines is essential for getting the most power when wind speeds change. Typical machine‑learning MPPT models can overfit or work poorly when they are used with data from many different weather conditions. This work looks at a validation approach that uses several data sets and a hybrid feature‑selection method for wind‑turbine MPPT. The hybrid method mixes filter, wrapper, and embedded feature‑selection techniques to pick the most important weather and electrical variables—such as wind speed, rotor speed, and generator temperature. It then tests these chosen variable groups on many different data sets to make sure the system works well everywhere and does not favor

any single case. Implementation includes building a model of the turbine’s dynamics, running MPPT controller simulations with changing wind patterns, and checking how efficiently the feature‑selection algorithm works. The study also provides a step‑by‑step way to measure controller response time, tracking efficiency, and computational cost. This method‑focused framework can be used as a reference for academic work. It gives guidance on setting up validation tests and choosing the best feature sets to make smart MPPT controllers more reliable and adaptable in today’s wind‑energy systems.

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