Akademik Veri Yönetim Sistemi
Electric Power Components and Systems, cilt.51, sa.15, ss.1584-1596, 2023 (SCI-Expanded)
The micro-grid (μ-grid) has picked up momentum worldwide with the ability to supply cost-effective, clean, and reliable electrical power to the present-day demand. The practical μ-grids are comprised of non-conventional and conventional sources such as wind turbine generators (WTG) and diesel generators (DG). Due to the encouragement of wind power which is exceedingly sporadic in nature and thus the frequency of the μ-grid is exceedingly vulnerable due to the erratic nature of wind speed. Variations in the load demand have also added to the vulnerability of the μ-grid at distinctive moments of time. Consequently, this paper appears to be a novel plan that utilizes artificial neuro-fuzzy inference system (ANFIS) within the built frequency regulation of the μ-grid. The proposed research has been employed within the μ-grid, and the application outcomes have taken all possibilities, such as load variety at the distinctive moment of time, modification of the load demand on the μ-grid, and step alteration of the wind input. The achieved results are coordinated with a few of the most recent results, which presents the ANFIS ahead over other strategies. Although there is a probable scope for improvement which subsequently involves the fuel cell (FC) with a hydrogen aqua electrolyzer (HAE) unit, as well as a redox flow battery (RFB), that is introduced one at a time in the μ-grid and the results of μ-grid are calculated for various working conditions to show the impact that the ANFIS technology has upon storage devices with regards to the μ-grid architecture.