Open AccessNeural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems
Author(s) -
Izhar Ul Haq,
Qudrat Khan,
Safeer Ullah,
Shahid A. Khan,
Rini Akmeliawati,
Mehmood Ashraf Khan,
Jamshed Iqbal
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0260480
Subject(s) - maximum power point tracking , photovoltaic system , control theory (sociology) , sliding mode control , maximum power principle , controller (irrigation) , computer science , backstepping , renewable energy , nonlinear system , control engineering , voltage , engineering , adaptive control , inverter , artificial intelligence , control (management) , physics , agronomy , quantum mechanics , electrical engineering , biology
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.