scholarly journals MPPT Based on Sinusoidal Extremum-Seeking Control in PV Generation

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
R. Leyva ◽  
C. Olalla ◽  
H. Zazo ◽  
C. Cabal ◽  
A. Cid-Pastor ◽  
...  
Keyword(s):  
Extremum Seeking ◽  
Control Technique ◽  
Maximum Power Point ◽  
Extremum Seeking Control ◽  
Pv Systems ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point ◽  
Stability Issues ◽  
Pv Generation

The paper analyses extremum-seeking control technique for maximum power point tracking circuits in PV systems. Specifically, the paper describes and analyses the sinusoidal extremum-seeking control considering stability issues by means a Lyapunov function. Based on this technique, a new architecture of MPPT for PV generation is proposed. In order to assess the proposed solution, the paper provides some experimental measurements in a 100 W prototype which corroborate the effectiveness of the approach.

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

2011 ◽  
Author(s):  
Xiao Li ◽  
Yaoyu Li ◽  
John E. Seem ◽  
Peng Lei
Keyword(s):  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Extremum Seeking ◽  
Solar Irradiation ◽  
Control Technique ◽  
Maximum Power Point ◽  
Extremum Seeking Control ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

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.

Maximum Power Point Tracking of Proton Exchange Membrane Fuel Cell With Fractional Order Filter and Extremum Seeking Control

2015 ◽  
Author(s):  
Jianxin Liu ◽  
Tiebiao Zhao ◽  
YangQuan Chen
Keyword(s):  
Proton Exchange Membrane ◽  
Proton Exchange ◽  
Extremum Seeking ◽  
Maximum Power Point ◽  
Pass Filter ◽  
Extremum Seeking Control ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point ◽  
Exchange Membrane

Proton Exchange Membrane FC (PEMFC) is widely recognized as a potentially renewable and green energy source based on hydrogen. Maximum power point tracking (MPPT) is one of the most important working conditions to be considered. In order to improve the searching performance such as convergence and robustness under disturbance and uncertainty, a kind of fractional order low pass filter (FOLPF) is applied for the MPPT controller design based on general Extremum Seeking Control (ESC). The controller is designed with FOLPF and high pass filter (HPF) substituting the normal LPF and HPF in the original ESC design. With this FOLPF ESC, better convergence and smooth performance is gained while maintaining the robust specifications. Simulation results are included to validate the proposed new FOLPF ESC scheme under disturbance and comparisons between FOLPF ESC and general ESC method are also provided.

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