Novel Lyapunov-based rapid and ripple-free MPPT using a robust model reference adaptive controller for solar PV system

Abstract

The technological, economic, and environmental benefits of photovoltaic (PV) systems have led to their widespread adoption in recent years as a source of electricity generation. However, precisely identifying a PV system's maximum power point (MPP) under normal and shaded weather conditions is crucial to conserving the maximum generated power. One of the biggest concerns with a PV system is the existence of partial shading, which produces multiple peaks in the P–V characteristic curve. In these circumstances, classical maximum power point tracking (MPPT) approaches are prone to getting stuck on local peaks and failing to follow the global maximum power point (GMPP). To overcome such obstacles, a new Lyapunov-based Robust Model Reference Adaptive Controller (LRMRAC) is designed and implemented to reach GMPP rapidly and ripple-free. The proposed controller also achieves MPP accurately under slow, abrupt and rapid changes in radiation, temperature and load profile. Simulation and OPAL-RT real-time simulators in various scenarios are performed to verify the superiority of the proposed approach over the other state-of-the-art methods, i.e., ANFIS, INC, VSPO, and P&O. MPP and GMPP are accomplished in less than 3.8 ms and 10 ms, respectively. Based on the results presented, the LRMRAC controller appears to be a promising technique for MPPT in a PV system.

A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems

“…As suggested in 71 , 72 , a small signal model of the studied system was considered (Fig. 4) in order to make it easier to analyze how the system responds to changes over time.…”

“…A resistor R with a small signal PV voltage represents the solar array Vpv and current I pv throughout its terminals, while battery storage represents the load R O . By ignoring the battery load, the transfer function between the duty cycle d (s) as an input and the array voltage V pv(s) as an output of the system can be described as following 72 :…”

MPPT techniques for photovoltaic systems: a systematic review in current trends and recent advances in artificial intelligence

“…• Proposing new GMPPT approaches using data-driven energy extraction and trained deep learning and/or machine learning models [53,54]. • Proposing novel nonlinear robust controllers to optimize energy extraction [55,56].…”

“…org/ docum ent/ 92123 70 shading, and continuously changing weather conditions [44]"), different hardware configurations, a variety of procedures, inputs, and perturbations. Figure 19 gives a graphical representation of such an example of structured methodology used to validate a new controller model for GMPP tracking [55].…”

Hybrid two‐stage adaptive maximum power point tracking for stand‐alone, grid integration, and partial shaded PV system

“…These computationally-based MPPT approaches are able to handle system nonlinearity in order to monitor the global MPP (GMPP), particularly under PSCs. [16][17][18] Despite this, the complexity and computational workload of soft-computing approaches remain the main challenges. As a result, the goal of this research is to create and implement a straightforward yet effective controller that can adapt to changing weather patterns and suppress the cutting-edge and traditional MPPT controller limitations.…”