A Comparative Analysis of Various Controller Techniques for Optimal Control of Smart Nano-Grid Using GA and PSO Algorithms

Shaharyar Yousaf; Abdullah Mughees; Muhammad Gufran Khan; Arslan Ahmed Amin; Muhammad Adnan

doi:10.1109/ACCESS.2020.3038021

IEEE Access (Jan 2020)

A Comparative Analysis of Various Controller Techniques for Optimal Control of Smart Nano-Grid Using GA and PSO Algorithms

Shaharyar Yousaf,
Abdullah Mughees,
Muhammad Gufran Khan,
Arslan Ahmed Amin,
Muhammad Adnan

Affiliations

Shaharyar Yousaf: ORCiD; Department of Electrical Engineering, National University of Computer and Emerging Sciences, Islamabad, Pakistan
Abdullah Mughees: ORCiD; Department of Electrical Engineering, National University of Computer and Emerging Sciences, Islamabad, Pakistan
Muhammad Gufran Khan: ORCiD; Department of Electrical Engineering, National University of Computer and Emerging Sciences, Islamabad, Pakistan
Arslan Ahmed Amin: ORCiD; Department of Electrical Engineering, National University of Computer and Emerging Sciences, Islamabad, Pakistan
Muhammad Adnan: ORCiD; Department of Electrical Engineering, National University of Computer and Emerging Sciences, Islamabad, Pakistan

DOI: https://doi.org/10.1109/ACCESS.2020.3038021
Journal volume & issue: Vol. 8
pp. 205696 – 205711

Abstract

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A nano-grid is an independent hybrid sustainable framework that utilizes non-renewable and renewable power resources for supplying continuous electrical energy to the load. Considering this scenario, in this research work, photovoltaic (PV) array, wind turbine, and fuel cell are taken as the three generation resources that have been used in the nano-grid. The active and reactive power of the all three generation resources is controlled using various controllers, i.e. integral, proportional-integral, proportional derivative, proportional integral derivative, fractional-order proportional-integral, fractional order proportional integral derivative (FOPID) and sliding mode controller (SMC). An advanced optimization technique based on a genetic algorithm (GA) and particle swarm optimization (PSO) algorithm has been utilized to optimize all of these controllers. The integral square error is taken as the objective function for both optimization algorithms. Finally, a graphical and tabular comparative analysis of all optimized controllers along with their control parameters and performance indexes is evaluated to find the best optimal solution. The performance of SMC has surpassed the performance of all other optimized controllers for power stability. In less than 0.267 seconds, the actual power yielded by using SMC is within 1% of the desired power. PSO algorithm has performed better than GA algorithm with all controllers. The worst performance is by FOPID controller with a steady state error of 6071.3W using GA algorithm and have a high magnitude of overshoot and undershoot. Moreover, a smart switching algorithm has been introduced for switching between the generation resources in accordance with the load demand and cost of the system in order to operate the nano-grid more economically. Finally, a case study has been performed in which the smart switching algorithm is utilized to switch to the best available generation resource in case of any fault at the generation side to provide uninterrupted power to the attached loads.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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