Recurrent Neuro-Fuzzy Control of Grid-Interfaced Solid Oxide Fuel Cell (SOFC) System

Abstract

The paper presents controller design, modelling, and simulations of the solid oxide fuel cell (SOFC) system. The SOFC model is used for the development of the fuzzy control scheme to improve the system performance. The SOFC is widely acknowledged for the clean distributed power generation. However, dire process problems occur frequently when the stand-alone fuel cell is directly interfaced with the electricity grid. Moreover, sustaining the optimal power quality and load following is the huge challenge, during the peak power demand schedule from the utility grid and large load perturbations. Consequently, a suitable and highly efficient control system is required for controlling and following the power load demands for the complex grid interfaced SOFC power systems. Therefore, a novel nonlinear hybrid adaptive recurrent fuzzy neural network (ARFNN) is developed for the control of the grid interfaced SOFC. The rapid power load following and safe SOFC operation requirement is also considered in the design of the closed loop control system. Simulation results proved that the proposed hybrid ARFNN enhances the optimal power quality and load-following than the conventional PI control scheme.