Enhancing Power Quality in Decentralized Hybrid Microgrids: Optimized DSTATCOM Performance Using Cascaded Fractional-Order Controllers and Hybrid Optimization Algorithms

Abdullah M. Alharbi; Sulaiman Z. Almutairi; Ziad M. Ali; Shady H. E. Abdel Aleem; Mohamed M. Refaat

doi:10.3390/fractalfract8100589

Fractal and Fractional (Oct 2024)

Enhancing Power Quality in Decentralized Hybrid Microgrids: Optimized DSTATCOM Performance Using Cascaded Fractional-Order Controllers and Hybrid Optimization Algorithms

Abdullah M. Alharbi,
Sulaiman Z. Almutairi,
Ziad M. Ali,
Shady H. E. Abdel Aleem,
Mohamed M. Refaat

Affiliations

Abdullah M. Alharbi: Department of Electrical Engineering, College of Engineering at Wadi Addawasir, Prince Sattam bin Abdulaziz University, Wadi Addawasir 11991, Saudi Arabia
Sulaiman Z. Almutairi: Department of Electrical Engineering, College of Engineering, Prince Sattam bin Abdulaziz University, Al Kharj 16278, Saudi Arabia
Ziad M. Ali: Department of Electrical Engineering, College of Engineering at Wadi Addawasir, Prince Sattam bin Abdulaziz University, Wadi Addawasir 11991, Saudi Arabia
Shady H. E. Abdel Aleem: Department of Electrical Engineering, Institute of Aviation Engineering and Technology, Giza 12658, Egypt
Mohamed M. Refaat: Department of Photovoltaic Cells, Electronics Research Institute, Cairo 11843, Egypt

DOI: https://doi.org/10.3390/fractalfract8100589
Journal volume & issue: Vol. 8, no. 10
p. 589

Abstract

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At present, the integration of microgrids into power systems presents significant power quality challenges in terms of the rising adoption of nonlinear loads and electric vehicles. Ensuring the stability and efficiency of the electrical network in this evolving landscape is crucial. This paper explores the implementation of cascading Proportional–Integral (PI-PI) and cascading Fractional-Order PI (FOPI-FOPI) controllers for a Distribution Static Compensator (DSTATCOM) in hybrid microgrids that include photovoltaic (PV) systems and fuel cells. A novel hybrid optimization algorithm, WSO-WOA, is introduced to enhance power quality. This algorithm leverages the strengths of the White Shark Optimization (WSO) algorithm and the Whale Optimization Algorithm (WOA), with WSO generating new candidate solutions and WOA exploring alternative search areas when WSO does not converge on optimal results. The proposed approach was rigorously tested through multiple case studies and compared with established metaheuristic algorithms. The findings demonstrate that the WSO-WOA hybrid algorithm significantly outperforms others in optimizing the PI-PI and FOPI-FOPI controllers. The WSO-WOA algorithm showed an improvement in accuracy, surpassing the other algorithms by approximately 7.29% to 14.1% in the tuning of the PI-PI controller and about 8.5% to 21.2% in the tuning of the FOPI-FOPI controller. Additionally, the results confirm the superior performance of the FOPI-FOPI controller over the PI-PI controller in enhancing the effectiveness of the DSTATCOM across various scenarios. The FOPI-FOPI provided controller a reduced settling time by at least 30.5–56.1%, resulting in marked improvements in voltage regulation and overall power quality within the microgrid.

Published in Fractal and Fractional

ISSN: 2504-3110 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science: Physics: Heat: Thermodynamics; Science: Mathematics: Analysis
Website: http://www.mdpi.com/journal/fractalfract

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