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Maximum Power Point Tracking for Photovoltaic Systems Using Adaptive Extremum Seeking Control

[+] Author Affiliations
Xiao Li, Yaoyu Li, Peng Lei

University of Wisconsin-Milwaukee, Milwaukee, WI

John E. Seem

Johnson Controls, Inc., Milwaukee, WI

Paper No. DSCC2011-6090, pp. 803-810; 8 pages
doi:10.1115/DSCC2011-6090
From:
  • ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control
  • ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, Volume 1
  • Arlington, Virginia, USA, October 31–November 2, 2011
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5475-4
  • Copyright © 2011 by ASME

abstract

Due to the relatively higher cost of energy (COE) for the photovoltaic (PV) systems, it is crucial to locate the maximum power point (MPP) so as to increase the system efficiency. The nonlinear PV characteristic curve and the MPP depend on PV’s intrinsic characteristics and environment conditions such as solar irradiation intensity and temperature. Maximum power point tracking (MPPT) control serves to seek the MPP of the PV system with the unpredicted environment uncertainties. In this paper, the adaptive extremum seeking control (AESC) scheme is investigated for the PV MPPT, which optimizes the duty ratio for the pulse-width modulator (PWM) of the DC-DC converter. The adopted AESC scheme utilizes an explicit structure information of the PV-buck system based on the system states and unknown PV characteristics. The radial basis function (RBF) neural network has been used to approximate the unknown nonlinear I-V curve. A Lyapunov-based adaptive learning control technique is used to ensure the convergence of the system to a neighborhood of the optimum which depends on the approximation error. The performance of the controller is verified through simulation.

Copyright © 2011 by ASME

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