Comprehensive analysis of classical and 2 DOF controllers for fuel cell powered nanogrids by Dwarf Mongoose algorithm

Abstract

Structural changes in electrical energy systems are occurring rapidly. Due to the development of technology and the increase in energy consumption, the importance and impact of nanogrids in the energy system is increasing. It is very difficult to sustain nanogrids as offgrid. Therefore, this study thoroughly examines the most efficient controller designs for nanogrids that use fuel cells as their primary source of energy. In addition, Dwarf Mongoose Optimization (DMO), a new effective optimization technique, is used to determine controller gains in nanogrid systems for the first time in the literature. Controller gains for different objective functions such as ISE, IAE, ITSE and ITAE are determined using classical PID controllers and two degrees of freedom (2 DOF) PID controllers. Moreover, sensitivity analysis is carried out for both variable and constant load scenarios. The constant load is selected as 0.25 pu, and variable load is chosen from values ranging from 0.15 pu to 0.75 pu. In addition, the response of the system to parameter changes is examined by increasing or decreasing the time constants in all load cases. The system parameters are changed from - 20 % to +40 % in 0.05 intervals. It has been noted that the suggested controller topologies perform differently depending on the objective functions. Thus, a significant contribution has been made to researchers to indicate the effectiveness of various control structures in fuel cell powered nanogrids.