Genetic Algorithm & Fuzzy Logic Based PEM Fuel Cells Power Conversion System for AC Integration
Shahid Naseem
UCEST, Lahore Leads University, Lahore, Pakistan; National College of Business Administration Lahore, Pakistan.
M.Irfan Abid
Riphah International University, Faisalabad, Pakistan
Fuad Usman
UCEST, Lahore Leads University, Lahore, Pakistan
Fahad Ahmad
National College of Business Administration & Economics, Lahore, Pakistan
Arslan Butt
UCEST, Lahore Leads University, Lahore, Pakistan
Farhan Aas
UCEST, Lahore Leads University, Lahore, Pakistan
Tahir Alyas
National College of Business Administration & Economics, Lahore, Pakistan
Keywords: DC-AC converter, Genetic Algorithm, Proton Exchange Membrane Fuel Cell, AC Integration, Fuzzy Logic Controller, PEMPC Simulation, Proton Exchange Membrane Power Optimizer.
Abstract
In the scientific environment, the leading variables such as voltage, current, power, heat from cooling system, membrane temperature and hydrogen pressure are uses as steady state and transient behaviors of Fuel Cells (FC). In the reproducing process of Fuel Cells (FC) variations, DC-DC converters are connected transversely its terminals, the efficiency, stability and durability are considered as operational problems for steady state. Since the Proton Exchange Fuel Cell is a non-linear process and its parameters change when it is delivering energy to the grid. The conventional controllers can’t content the control objectives. In this paper, an intelligent DC-AC power optimization is proposed for Fuel Cell (FC) control system to produce energy in the grid stations and to improve the power quality when FC is supplying load to grid. Furthermore, a Genetic Algorithm (GA) based reactive power optimization for voltage profile improvement and real power minimization in DC-AC system. A fuzzy logic controller is also used to control active power of PEM fuel cell system. Fuzzy logic controller will modify the hydrogen flow feedback from the terminal load. At the end, we will simulate DC-AC converter for checking its efficiency, stability and durability on the basis of the genetic algorithm and fuzzy logic controller to control power generation.