| Abstract: |
| Energy production from renewable sources offers an efficient alternative non-polluting and sustainable solution.
Among renewable energies, solar energy represents the most important source, the most efficient and the least
expensive compared to other renewable sources. Electric power generation systems from the sun’s energy typically
characterized by their low efficiency. However, it is known that photovoltaic pumping systems are the most
economical solution especially in rural areas. This work deals with the modeling and the vector control of a solar
photovoltaic (PV) pumping system. The main objective of this study is to improve optimization techniques that
maximize the overall efficiency of the pumping system. In order to optimize their energy efficiency whatever, the
weather conditions, we inserted between the inverter and the photovoltaic generator (GPV) a maximum power
point adapter known as Maximum Power Point Tracking (MPPT). Among the various MPPT techniques presented in
the literature, we adopted the adaptive neuro-fuzzy controller (ANFIS). In addition, the performance of the sliding
vector control associated with the neural network was developed and evaluated. Finally, simulation work under
Matlab / Simulink was achieved to examine the performance of a photovoltaic conversion chain intended for
pumping and to verify the effectiveness of the speed control under various instructions applied to the system.
According to the study, we have done on the improvement of sliding mode control with neural network. Note that
the sliding-neuron control provides better results compared to other techniques in terms of improved chattering
phenomenon and less deviation from its reference. |
| Key words: Maximum power point tracking (MPPT), Adaptive Neuro-fuzzy inference systems (ANFIS, Photovoltaic(PV) systems, Fuzzy logic controller (FLC), Pump, Sliding mode controller |
| DOI：10.1186/s41601-019-0145-1 |
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| Fund: |
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