Neural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems

Haq, Izhar Ul; Khan, Qudrat; Ullah, Safeer; Ahmed Khan, Shahid; Akmeliawati, Rini; Khan, Mehmood Ashraf; Iqbal, Jamshed

Abstract

The increasing energy demand and the target to reduce environmental pollution make it essential to use efficient and environment-friendly renewable energy systems. One of these systems is the Photovoltaic (PV) system which generates energy subject to variation in environmental conditions such as temperature and solar radiations. In the presence of these variations, it is necessary to extract the maximum power via the maximum power point tracking (MPPT) controller. This paper presents a nonlinear generalized global sliding mode controller (GGSMC) to harvest maximum power from a PV array using a DC-DC buck-boost converter. A feed-forward neural network (FFNN) is used to provide a reference voltage. A GGSMC is designed to track the FFNN generated reference subject to varying temperature and sunlight. The proposed control strategy, along with a modified sliding mode control, eliminates the reaching phase so that the sliding mode exists throughout the time. The system response observes no chattering and harmonic distortions. Finally, the simulation results using MATLAB/Simulink environment demonstrate the effectiveness, accuracy, and rapid tracking of the proposed control strategy. The results are compared with standard results of the nonlinear backstepping controller under abrupt changes in environmental conditions for further validation.

Citation

Haq, I. U., Khan, Q., Ullah, S., Ahmed Khan, S., Akmeliawati, R., Khan, M. A., & Iqbal, J. (2022). Neural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems. PLoS ONE, 17(1), Article e0260480. https://doi.org/10.1371/journal.pone.0260480

• Journal Article Type: Article
• Acceptance Date: Nov 10, 2021
• Online Publication Date: Jan 20, 2022
• Publication Date: Jan 20, 2022
• Deposit Date: Nov 15, 2021
• Publicly Available Date: Oct 27, 2022
• Journal: PLoS ONE
• Print ISSN: 1932-6203
• Electronic ISSN: 1932-6203
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 1
• Article Number: e0260480
• DOI: https://doi.org/10.1371/journal.pone.0260480
• Public URL: https://hull-repository.worktribe.com/output/3882709