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• W2912791683 startingPage "1142" @default.
• W2912791683 abstract "This paper presents the experimental implementation of a LMS-Adaline-based ANFIS controller of an improved power-quality photovoltaic (PV) generating system connected to the grid. The proposed system applies an adaptive neuro-fuzzy inference system (ANFIS) to control the DC–DC boost converter integrated with PV to achieve the maximum power point tracking (MPPT) operating condition. For power-quality improvement at the point of common coupling (PCC), Adaline (adaptive linear element)-based control algorithm is used to estimate the reference grid currents. To achieve high performance with fast dynamic response during transition and to regulate constant the DC and the AC voltages without saturation phenomena, ANFIS controller is employed. The real-time benchmark realised in the laboratory, to implement the setup, uses a dSPACE controller. To demonstrate the performance of the proposed configuration, the system is first simulated offline under numerous critical scenarios. The experimental results are then presented to validate the concept." @default.
• W2912791683 created "2019-02-21" @default.
• W2912791683 creator A5070590654 @default.
• W2912791683 creator A5070646902 @default.
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• W2912791683 date "2019-03-04" @default.
• W2912791683 modified "2023-10-14" @default.
• W2912791683 title "Real‐time experimental implementation of an LMS‐adaline‐based ANFIS controller to drive PV interfacing power system" @default.
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• W2912791683 doi "https://doi.org/10.1049/iet-rpg.2018.5245" @default.
• W2912791683 hasPublicationYear "2019" @default.
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