Performance enhancement and power management strategy of an autonomous hybrid fuel cell/wind power system based on adaptive neuro fuzzy inference system

Abstract

In this paper, a hybrid wind/fuel cell generation system which can be used for loads in remote areas as a micro grid application is considered. This micro grid mainly includes fuel cell (FC), wind generator as electrical power suppliers, resistive-inductive impedance as static load, induction motor (IM) as a dynamic load, DC/AC converter and water electrolizer for supplying hydrogen gas. The Fuel cell is used to compensate the decrease in the power generated by wind, which leads to an increase in the system efficiency. Furthermore, an adaptive control model and achievement refinements of a micro-grid using Adaptive Neuro Fuzzy Inference System (ANFIS) controller has been utilized to regulate the load voltage and frequency. This suggested microgrid system is achieved so that the wind generation unit supplies the loads, while any additional energy needed by the loads will be offset by the fuel cell generator unit. Thus, the main objective of this work is to apply an adaptive control method for improving the proposed electrical micro grid performance. In addition, the performance of the considered system is compared with the proposed ANFIS control when applying the traditional fuzzy control. The outcomes also demonstrated a better reaction and durability to the chosen control model. The MATLAB/SIMULINK programming software tools have been used for carrying out case studies towards the evaluation and validation of the methodology developed in this work with applications. The proposed solution achieved improvement in transient performance. However, the settling time is decreased to 21% in the case of using the suggested ANFIS controller comparing with conventional fuzzy control.