Abstract
Due to the increased complexity and nonlinear nature of microgrid systems such as photovoltaic, wind-turbine fuel cell, and energy storage systems (PV/WT/FC/ESSs), load-frequency control has been a challenge. This paper employs a self-tuning controller based on the fuzzy logic to overcome parameter uncertainties of classic controllers, such as operation conditions, the change in the operating point of the microgrid, and the uncertainty of microgrid modeling. Furthermore, a combined fuzzy logic and fractional-order controller is used for load-frequency control of the off-grid microgrid with the influence of renewable resources because the latter controller benefits robust performance and enjoys a flexible structure. To reach a better operation for the proposed controller, a novel meta-heuristic whale algorithm has been used to optimally determine the input and output scale coefficients of the fuzzy controller and fractional orders of the fractional-order controller. The suggested approach is applied to a microgrid with a diesel generator, wind turbine, photovoltaic systems, and energy storage devices. The comparison made between the results of the proposed controller and those of the classic PID controller proves the superiority of the optimized fractional-order self-tuning fuzzy controller in terms of operation characteristics, response speed, and the reduction in frequency deviations against load variations.
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Funding
The authors gratefully acknowledge financial support from the Universiti Teknologi Malaysia (Post-Doctoral Fellowship Scheme grant 05E09, and RUG grants 01M44, 02M18, 05G88, 4B482) and Post-Doctoral fellow (Teaching & Learning) Scheme under MJIIT-UTM.
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Amirreza Naderipour: Writing—original draft, conceptualization, methodology. Aldrin Abdullaha: Supervision. Iraj Faraji Davoodkhani: Codding, software. Zulkurnain Abdul-Malek: validation. Hesam Kamyab: Writing—reviewing and editing.
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Naderipour, A., Abdul-Malek, Z., Davoodkhani, I.F. et al. Load-frequency control in an islanded microgrid PV/WT/FC/ESS using an optimal self-tuning fractional-order fuzzy controller. Environ Sci Pollut Res 30, 71677–71688 (2023). https://doi.org/10.1007/s11356-021-14799-1
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DOI: https://doi.org/10.1007/s11356-021-14799-1