Real- Time implementation of a PV system maximum power point tracking based on the ANN-Backstepping sliding mode control.

Rafika EL idrissi, Ahmed Abbou, Mohcine Mokhlis, Hicham Bouzakri, Yassine El houm

Abstract


This work presents a real-time implementation of the maximum power point tracking (MPPT) of the PV system using an ANN-BSMC controller. Effectively, this hybrid control consists of double stages, namely: the artificial neural network (ANN) and the backstepping-sliding mode control (BSMC). The first one can predict the optimum voltage corresponding to the maximum power of the PV module while the second one serves for adjusting the duty cycle of the DC/DC boost converter to follow the voltage given. Test-bench components include a 20W PV module, boost converter with a resistive load of 50, current sensor (ACS712ELC-05B), input voltage and designed output voltage sensors of and 0-35V voltage range respectively, temperature and irradiation sensors, as well as NI-DAQ 6321 data collection board required to execute the hybrid control. The system stability is proved using Lyapunov functions. The applied approach is compared to the P&O-BSMC in real-time under the same weather conditions. The comparison efficiency was performed under two experimental tests. In both results, the ANN-BSMC shows a high dynamic response in terms of tracking rapidity, oscillation around the MPP, steady-state error, and the PV system efficiency. Based on the results, the ANN-BSMC technique could reach the optimum value in about 0.3s while the P&O-BSMC could reach it until 0.7s. As well as the efficiency of the proposed technique is 83%. 


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DOI (PDF): https://doi.org/10.20508/ijrer.v11i4.12386.g8348

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