ROBUST SLIDING-MODE-BASED LEARNING FOR MAXIMUM POWER POINT TRACKING PHOTOVOLTAICS SYSTEM - Abdal-Razak Shehab Hadi; Adnan Alamili; Ali Abdyasser Kadhum (Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ)
Maximum power point tracking (MPPT) is crucial for optimizing the energy extraction from solar modules in photovoltaic (PV) systems. This paper focuses on maximizing the energy extraction from solar panels and explores the important aspects of MPPT technology in PV systems. The sliding mode learning controller (SMLC) created for MPPT provides a new way to deal with environmental factors, including radiation and temperature fluctuations. The study compares the disturbance-insensitive SMLC with linear proportional-integral-derivative (PID) controllers and conventional sliding mode controllers (CSMC). The SMLC enhances the maximum energy extraction by tracking the reference voltage signal using the perturb and observe (P&O) algorithm. Moreover, the controller can adapt to the dynamic changes in PV characteristics thanks to the learning system. The result shows that the proposed SMLC offers significant benefits, especially in challenging operating conditions. It demonstrates superior vibration-free performance, fast response (with a settling time of 24.7 ms), and smooth and precise tracking compared to other controllers.