Real‐time experimental implementation of an LMS‐adaline‐based ANFIS controller to drive PV interfacing power system

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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.

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