FPGA control system technology for integrating the PV/wave/FC hybrid system using ANN optimized by MFO techniques

Abstract

Despite the merits of Renewable Energy Sources (RES) as clean and reliable alternatives for electrical energy generation, there are some problems related to their highly cost and low efficient operation with non – linear loads. As one of the most vital methodologies to improve the operational quality of RES within the electrical network, the integration of RES in a one hybrid system can effectively contribute to overcome the obstacles of the randomness and inability to accurately predict the daily generation of each individual RES. In this research, Fuel Cells (FCs) are practically integrated with two renewable sources (wave energy and solar energy) utilizing the Field Programmable Gate Array (FPGA) as a new innovative digital controller technique. FPGAs are chosen in this study for its ultra – fast processing speed that is expected to reach to almost 100MHz and its higher response than other microcontrollers for RES integration. Although the merits of FPGAs like fast response, having no processors and behaving in a parallel manner, there are some obstacles in controlling the energy level of RES during the integration process. To overcome the problems of FPGAs, Moth Flame Optimization (MFO) algorithm is utilized with Artificial Neural Network (ANN) to enhance its operational accuracy for providing an effective and precise forecasting control scenario for the proposed hybrid system. In this paper, FCs are provided as an effective Battery Energy Storage Systems (ESS) to overcome the sudden operational outage of any RES to ensure the reliability of the proposed hybrid system within the electrical network. This research provides the hybrid combination between the Buck – Boost converter and FPGA as a vital approach to adjust the voltage level of the proposed RES integration within a reasonable value. In this research, all the obtained results are assessed based on the available previous simulation and empirical data to confirm the validity and high response of the FPGA technique in RES integration.