Title :
Maximum Power Point Tracking for Photovoltaic System Using Adaptive Extremum Seeking Control
Author :
Xiao Li ; Yaoyu Li ; Seem, J.E.
Author_Institution :
Dept. of Mech. Eng., Univ. of Wisconsin-Milwaukee, Milwaukee, WI, USA
Abstract :
In order for photovoltaic (PV) systems to maximize their efficiency of power generation, it is crucial to locate the maximum power point (MPP) in real time under realistic illumination conditions. The current-voltage (I-V) characteristics of PV devices are nonlinear, and the MPP may vary with intrinsic and environmental conditions. Maximum power point tracking (MPPT) control is expected to seek the MPP regardless of the device and ambient changes. This brief presents the application of the adaptive extremum seeking control (AESC) scheme to the PV MPPT problem. A state-space model is derived via averaging method, with the control input being the duty ratio of the pulse-width modulator of the dc-dc buck converter. To address the nonlinear PV characteristics, the radial basis function 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 AESC is verified with simulation.
Keywords :
Lyapunov methods; PWM power convertors; adaptive control; environmental factors; lighting; maximum power point trackers; optimal control; photovoltaic cells; photovoltaic power systems; radial basis function networks; state-space methods; AESC scheme; Lyapunov-based adaptive control method; MPPT control; PV MPPT problem; PV devices; PV systems; adaptive extremum seeking control scheme; averaging method; current-voltage characteristics; dc-dc buck converter; environmental conditions; intrinsic conditions; maximum power point tracking; maximum power point tracking control; nonlinear I-V curve; nonlinear PV characteristics; photovoltaic system; power generation efficiency; pulse-width modulator; radial basis function neural network; realistic illumination conditions; state-space model; Adaptive control; Approximation methods; Convergence; Maximum power point trackers; Photovoltaic systems; Radial basis function networks; Adaptive extremum seeking control; averaging model; maximum power point tracking; radial basis function; solar photovoltaics;
Journal_Title :
Control Systems Technology, IEEE Transactions on
DOI :
10.1109/TCST.2012.2223819
