The Effect of PEMFC on Power Grid Using Advanced Equilibrium Optimizer and Particle Swarm Optimisation for Voltage Sag Mitigation

Due to the integration of various distributed generation resources into power systems, in particular, the integration of proton exchange membrane fuel cell (PEMFC), the electrical power grid (EPG) becomes more comprehensive and has power quality (PQ) issues like voltage/current unbalance, harmonics, voltage sag, etc. The issue of voltage sag arises from sudden changes in the EPG operating conditions. In this paper, the Advanced Equilibrium Optimizer (AEO) and Particle Swarm Optimization (PSO) are proposed as solutions for EPG disturbance in partially voltage sag issues caused by three fault scenarios: single line-to-ground faults (SLGF), double line-to-ground faults (DLGF), and three line-to-ground faults (TLGF). Both of proposed controllers are set up by applying AEO and PSO, and their preponderances are demonstrated by comparison with a conventional PI controller. The simulink model of PEMFC-EPG interconnection system is carried out in MATLAB/Simulink environment. The results of PEMFC-EPG interconnection using AEO and PSO are impressive and persuasive in terms of the voltage sag elimination.

[1]  S. M. Zali,et al.  An Integrated of Hydrogen Fuel Cell to Distribution Network System: Challenging and Opportunity for D-STATCOM , 2021, Energies.

[2]  Mohamed Nayel,et al.  Optimal Placement and Sizing of Wind Turbine Generators and Superconducting Magnetic Energy Storages in a Distribution System , 2021 .

[3]  D. Kodirov,et al.  Fault control of microgrid system: A case study of Karabuk University - Turkey , 2020, IOP Conference Series: Earth and Environmental Science.

[4]  Bindeshwar Singh,et al.  A comprehensive survey on enhancement of system performances by using different types of FACTS controllers in power systems with static and realistic load models , 2020 .

[5]  Yun Han,et al.  A review modeling of optimal location and sizing integrated M–FACTS with wind farm and fuel cell , 2020 .

[6]  Fu-Kwun Wang,et al.  Bi-directional long short-term memory recurrent neural network with attention for stack voltage degradation from proton exchange membrane fuel cells , 2020 .

[7]  Xiaopeng Li,et al.  A microgrids energy management model based on multi-agent system using adaptive weight and chaotic search particle swarm optimization considering demand response , 2020 .

[8]  Fernando Morgado Dias,et al.  Particle Swarm Optimisation: A Historical Review Up to the Current Developments , 2020, Entropy.

[9]  R. Chacartegui,et al.  Hybrid solar power plant with thermochemical energy storage: A multi-objective operational optimisation , 2020, Energy Conversion and Management.

[10]  Henrik Madsen,et al.  Stochastic model of wind-fuel cell for a semi-dispatchable power generation , 2017 .

[11]  Abdussalam Ali Ahmed,et al.  A Review of Fuel Cell to Distribution Network Interface Using D-FACTS: Technical Challenges and Interconnection Trends , 2021, International Journal of Electrical and Electronic Engineering & Telecommunications.