An adaptive power point tracker in wind photovoltaic system using an optimized deep learning framework

Over the past two decades, the integration and generation of photovoltaic power plants have risen to a huge level. The reason is increased pollution, which tends to cause ozone damage. The input source is a wind turbine, in which wind converts mechanical energy into electrical energy. The grid and solar array links have been optimized using the Maximum Power Point Tracker (MPPT), demonstrating the DC to DC converter. The deep learning framework could improve the performance and efficiency of MPPT. The RLC filter has been utilized for the specialized deduction in the current harmonic and voltage. The complexity, voltage fluctuation, and load imbalance were the causes to reduce power quality performance. Therefore, the proposed method has been designed to get error-free output. Furthermore, the MPPT based on a novel Vulture-based convolution neural model (VbCNM) is utilized to improve the efficiency and extract the power. The proposed method has resolved the open issues and technical implementation using the VBCNN technique. Furthermore, the precise error-free output has been determined by simulating MATLAB. The results obtained are more efficient, fast convergence, and clear. Finally, the simulation output was compared with various conventional methods and has attained a lower power loss as 0.1 kW and less sample times as 9 µs.