Design and Analysis of Reccurent Neural Network (RNN) MPPT Controller for Hybrid Solar and Wind Energy System

In the next few years, rising energy consumption will make it easier to use new energy technologies to create durable electrical constructions. When compared to a single converter, the hybrid photovoltaic and wind energy system can connect integrated input sources using the Multi Input DC-DC Converter topology, which can maximize the output from both input sources. The Recurrent Neural Network (RNN) algorithm is used in the field of Artificial Intelligence (AI) to provide a faster optimization response than traditional methods. This methodology is implemented on PSIM software. According to the results, a significant impact on system efficiency is attributed to the success of the Maximum Power Point Tracking (MPPT) technique employing the RNN algorithm. The efficient RNN method can track variations in wind speed and sun radiation with a settling time 0.087 seconds quicker than the PnO (Perturb and Observe) algorithm, according to the conclusions obtained from both parts. Even though a drop in wind speed can result in a decrease in PV (Photovoltaic) power during analysis of different scenarios of the system integrated with multi-input dc-dc converter, the RNN algorithm improves the conventional algorithm in power tracking approximately 10.2%