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In wind energy systems, wind speed estimation plays an important role. For wind energy systems, accurate forecasting of wind direction is essential, but it is challenging because of its variability. In this paper, wind speed prediction is accomplished using a machine learning-based random forest (RF) method. For the production of wind energy, short-term wind speed prediction is a significant activity. However, it is difficult only to obtain deterministic estimation since wind supplies are erratic and unpredictable. It increases learning that helps to project future values. Average wind speed is a major feature that affects the atmosphere. This paper explains the estimation of wind speed with ML algorithm. It is valuable for assessing the prospects for abnormal climate events and wind energy in the future.

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In this paper, the bidirectional converter that performs DC-DC power conversion is integrated with the variable speed wind energy conversion system’s grid system. Generally, the voltage quality is reduced, and fluctuation in outputs is performed in renewable energy sources. The stable operation and power generation with fluctuation and power demand in the variable are ensured by developing the energy management system (EMS). In this proposed method, the bidirectional converter’s control is accomplished using the proportional-integral-derivative (PID) controller, which controls the power fluctuation from the WECS to maintain constant. The DC-AC voltage conversion is achieved using the model predictive controller (MPC), which controls both power and voltage. This proposed power and voltage controller enhances the stable AC voltage supply and improves the proper power flow. The proposed method, which is validated in simulation is simple, and the results are obtained using MATLAB/Simulink.