Abstract:
To maintain the maximum achievable efficiency for the photovoltaic (PV) systems, it is crucial to achieve the maximum power point tracking (MPPT) operation for realistic ...Show MoreMetadata
Abstract:
To maintain the maximum achievable efficiency for the photovoltaic (PV) systems, it is crucial to achieve the maximum power point tracking (MPPT) operation for realistic illumination conditions. This paper presents the application of the adaptive extremum seeking control (AESC) scheme to the PV MPPT problem. A state-space model is derived for the PV system with buck converter. The AESC is used to maximize the power output by tuning the duty ratio of the pulse-width modulator (PWM) of the DC-DC buck converter. To address the nonlinear PV characteristics, the radial basis function (RBF) neural network is used to approximate the unknown nonlinear I-V curve. The convergence of the system to an adjustable neighborhood of the optimum is guaranteed by utilizing a Lyapunov-based adaptive control method. The performance of the controller is verified through simulations.
Date of Conference: 12-15 December 2011
Date Added to IEEE Xplore: 01 March 2012
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