Wind energy is one of the most promising renewable energy systems, offering a sustainable solution to fulfill the increasing energy requirements. One of the problems with renewable energy sources, like wind energy, is the presence of fluctuations and ripples in the power output, which can adversely affect the performance and efficiency of the system. So, in this work, a wavelet decomposition-based ripple extraction technique has been implemented to address this issue. Firstly, the Perturb & Observe and Fuzzy Logic Controller-based maximum power point tracking (MPPT) techniques are applied to the Permanent Magnet Synchronous Generator-based wind turbine to extract the maximum power from the wind energy system. These two MPPT techniques are compared with each other to evaluate their performance at variable wind speeds. The Fuzzy Logic Controller-based MPPT method demonstrated better performance, achieving 4.4% higher efficiency compared to the conventional Perturb & Observe MPPT at rated wind speed. Despite the good performance of these MPPT techniques, noticeable ripples are observed in the output power. To tackle this issue, a wavelet-based ten-level decomposition method is utilized to extract ripples from the output power. Simulation results show that the Fuzzy Logic Controller-based MPPT combined with the wavelet-based method achieves a higher percentage of overall ripple reduction, approaching up to 98.89% at higher decomposition levels, compared to 97.62% for Perturb & Observe with wavelet. The key contribution of this work is the development of an effective ripple suppression technique using a wavelet decomposition with MPPT techniques for enhanced maximum power extraction compared to conventional approaches.
Singh et al. (Tue,) studied this question.