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• W4316035710 endingPage "120652" @default.
• W4316035710 startingPage "120652" @default.
• W4316035710 abstract "Photovoltaic (PV) panel generator is inherently fluctuant due to its dependence on weather. If this generator connects to power grids or microgrids (MGs), the frequency of renewable-energy-based power grids or MGs will not be stable. Hence, in this paper, a dual-driven predictive control scheme is proposed to control the frequency to make MGs stable. First, a data-based frequency model is developed for a PV-diesel MG by using the Gaussian process (GP) method. Based on this frequency model, a virtual inertia controller is designed to provide inertia to the MG and a model-data (dual)-driven predictive frequency controller is proposed to further eliminate the frequency deviation. Since predictive control works in an optimisation framework, the design of the frequency controller is then transferred into a constrained optimisation problem and an optimal solution to this problem is obtained by using recurrent neural network (RNN) method. The simulations are carried out by comparing the proposed controller with the P, PI, PID and the model predictive controller. The results show that the proposed dual-driven controller can regulate the frequency with less overshoot and less root-mean-square error (RMSE) of the frequency deviation." @default.
• W4316035710 created "2023-01-14" @default.
• W4316035710 creator A5020065141 @default.
• W4316035710 creator A5051535527 @default.
• W4316035710 date "2023-03-01" @default.
• W4316035710 modified "2023-10-15" @default.
• W4316035710 title "A dual-driven predictive control for photovoltaic-diesel microgrid secondary frequency regulation" @default.
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• W4316035710 doi "https://doi.org/10.1016/j.apenergy.2023.120652" @default.
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