Maximum Power Point Tracking Approaches for Wind–Solar Hybrid Renewable Energy System—A Review

Control Strategy of a Hybrid Renewable Energy System Based on Reinforcement Learning Approach for an Isolated Microgrid

“…Finally, a new hybrid method for MPPT control, which integrates the Q-learning and perturbation and observation (P&O) methods, was proposed to improve system performance. P&O is the mostly preferred algorithm for MPPT control [14,15]. The major advantages of this method are simple structure and ease of implementation.…”

“…But P&O turned out to be ineffective under the fast change of the temperature and irradiation, as well as the partial shading conditions. A large step size of the P&O duty cycle (D) provides fast convergence with poor tracking while the low step size duty cycle provides low convergence with the ability to reduce the oscillation at the maximum power point [15]. The reinforcement learning approach to solve the MPPT problem aims to learn the system behavior based on the PV source response.…”

“…How to smoothly move from one MPP to another for rapid weather condition change. Based on numerous studies of MPPT in the last few decades, the comparison between these approaches are shown as follows [14,15] [47]. The drawbacks of these methods are that they need a large computer memory for training and the rule implementation.…”

“…Among these methods, PSO has become more commonly used in this field due to its easy implementation, simplicity, and robustness. Besides, it can combine with other methods to create new approaches [15,47].…”

“…Hybrid methods which integrate two or more MPPT algorithms together have a better performance and utilize the advantages of each method such as PSO-P&O, and PSO-GA [15]. The advantage of these methods is that they can help to track the global maximum power point quickly under the partial shading conditions.…”