Optimal Voltage and Frequency Control of an Islanded Microgrid Using Grasshopper Optimization Algorithm

Touqeer Ahmed Jumani, Mohd Wazir Mustafa, Madihah Md Rasid, Nayyar Hussain Mirjat, Zohaib Hussain Leghari and M. Salman Saeed
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Touqeer Ahmed Jumani: School of Electrical Engineering, University Technology Malaysia, Skudai, 81310 Johor Bahru, Malaysia
Mohd Wazir Mustafa: School of Electrical Engineering, University Technology Malaysia, Skudai, 81310 Johor Bahru, Malaysia
Madihah Md Rasid: School of Electrical Engineering, University Technology Malaysia, Skudai, 81310 Johor Bahru, Malaysia
Nayyar Hussain Mirjat: Department of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro 76090, Pakistan
Zohaib Hussain Leghari: School of Electrical Engineering, University Technology Malaysia, Skudai, 81310 Johor Bahru, Malaysia
M. Salman Saeed: School of Electrical Engineering, University Technology Malaysia, Skudai, 81310 Johor Bahru, Malaysia

Energies, 2018, vol. 11, issue 11, 1-20

Abstract: Due to the lack of inertia and uncertainty in the selection of optimal Proportional Integral (PI) controller gains, the voltage and frequency variations are higher in the islanded mode of the operation of a Microgrid (MG) compared to the grid-connected mode. This study, as such, develops an optimal control strategy for the voltage and frequency regulation of Photovoltaic (PV) based MG systems operating in islanding mode using Grasshopper Optimization Algorithm (GOA). The intelligence of the GOA is utilized to optimize the PI controller parameters. This ensures an enhanced dynamic response and power quality of the studied MG system during Distributed Generators (DG) insertion and load change conditions. A droop control is also employed within the control architecture, alongside the voltage and current control loops, as a power-sharing controller. In order to validate the performance of the proposed control architecture, its effectiveness in regulating MG voltage, frequency, and power quality is compared with the precedent Artificial Intelligence (AI) based control architectures for the same control objectives. The effectiveness of the proposed GOA based parameter selection method is also validated by analyzing its performance with respect to the improved transient response and power quality of the studied MG system in comparison with that of the Particle Swarm Optimization (PSO) and Whales Optimization Algorithm (WOA) based parameter selection methods. The simulation results establish that the GOA provides a faster and better solution than PSO and WOA which resulted in a minimum voltage and frequency overshoot with minimum output current and Total Harmonic Distortion (THD).

Keywords: grasshopper optimization algorithm; microgrid; power quality; voltage and frequency control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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