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Appl. Sci. 2017, 7(1), 95; doi:10.3390/app7010095

Analysis of MPPT Failure and Development of an Augmented Nonlinear Controller for MPPT of Photovoltaic Systems under Partial Shading Conditions

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
These authors contributed equally to this work.
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Author to whom correspondence should be addressed.
Academic Editor: Chien-Hung Liu
Received: 14 December 2016 / Revised: 12 January 2017 / Accepted: 14 January 2017 / Published: 19 January 2017
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Abstract

The output–voltage–power curves of photovoltaic (PV) arrays exhibit complex multi-peak shapes when local shading occurs. The existing maximum power point tracking (MPPT) algorithms to solve this multi-peak problem do not consider the possibility of tracking failures due to the time of the irradiance change. In this study, first, the reason for the failure of the global MPPT (GMPPT) algorithm is analyzed based on the PV array mathematical model and its output characteristics under partial shading conditions; then, in order to estimate the MPP voltage, an artificial neural network (ANN) is trained using environmental information such as irradiance. A hybrid MPPT method using an augmented state feedback precise linearization (AFL) controller combined with an ANN is proposed to solve problems such as the shift of the static operating point of the DC/DC boost converter. Finally, numerical simulations are conducted to validate the proposed method and eliminate the possibility of MPPT failure. The proposed hybrid MPPT method is compared with the conventional perturb and observe (P & O) method and the improved P & O method through simulations. Using the proposed neural network and nonlinear control strategy, the MPP can be tracked rapidly, accurately, and statically, proving that the method is feasible and effective. View Full-Text
Keywords: photovoltaic (PV) systems; partial shading condition (PSC); maximum power point tracking (MPPT); artificial neural network (ANN); nonlinearity controller; augmentation system photovoltaic (PV) systems; partial shading condition (PSC); maximum power point tracking (MPPT); artificial neural network (ANN); nonlinearity controller; augmentation system
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chen, M.; Ma, S.; Wu, J.; Huang, L. Analysis of MPPT Failure and Development of an Augmented Nonlinear Controller for MPPT of Photovoltaic Systems under Partial Shading Conditions. Appl. Sci. 2017, 7, 95.

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