Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems

Suliang Ma, Mingxuan Chen, Jianwen Wu, Wenlei Huo and Lian Huang
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Suliang Ma: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Mingxuan Chen: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Jianwen Wu: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Wenlei Huo: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Lian Huang: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China

Energies, 2016, vol. 9, issue 12, 1-24

Abstract: Photovoltaic (PV) systems have non-linear characteristics that generate maximum power at one particular operating point. Environmental factors such as irradiance and temperature variations greatly affect the maximum power point (MPP). Diverse offline and online techniques have been introduced for tracking the MPP. Here, to track the MPP, an augmented-state feedback linearized (AFL) non-linear controller combined with an artificial neural network (ANN) is proposed. This approach linearizes the non-linear characteristics in PV systems and DC/DC converters, for tracking and optimizing the PV system operation. It also reduces the dependency of the designed controller on linearized models, to provide global stability. A complete model of the PV system is simulated. The existing maximum power-point tracking (MPPT) and DC/DC boost-converter controller techniques are compared with the proposed ANN method. Two case studies, which simulate realistic circumstances, are presented to demonstrate the effectiveness and superiority of the proposed method. The AFL with ANN controller can provide good dynamic operation, faster convergence speed, and fewer operating-point oscillations around the MPP. It also tracks the global maxima under different conditions, especially irradiance-mutating situations, more effectively than the conventional methods. Detailed mathematical models and a control approach for a three-phase grid-connected intelligent hybrid system are proposed using MATLAB/Simulink.

Keywords: photovoltaic (PV) systems; DC/DC converter; maximum power-point tracking (MPPT); artificial neural network (ANN); non-linear controller; augmentation system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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