## 4 The Proposed h-POQL MPPT Control  
### The Assessment of the MPPT Control Methods  
The power generated by the PV and wind turbine systems is strongly dependent on the weather conditions. Thus, the hybrid system requires power converters to change the power forms and to transfer efficiently by applying MPPT techniques to extract maximum energy from wind and solar. The following process is the concept of MPPT control. * In Figure 12a, based on a typical solar radiation and temperature, there is a unique maximum power point (MPP) on the power-voltage (P-V) curve where the system can operate at the maximum efficiency and produce maximum power. Similar to PV system, the wind turbine produces maximum output power at a specific point of P-\(\omega_{m}\) curve as shown on the right hand side of Figure 12b. Thus, it is necessary to continuously track the MPP in order to maximize the output power. In generally, the major tasks of MPPT controller include: 1. How to quickly find the MPP. 2. How to stably stay at the MPP. 3. 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]:  
* Conventional methods, such as Perturbation & Observation (P&O), Incremental Conductance (IC), Open Circuit Voltage (OV), and Short Circuit Current (SC), are famous for their easy implementation, but their disadvantages are that they are poor convergence, slow tracking speed, and high steady-state oscillations. In contracts, AI methods are complicated in design and require high computing power. However, due to the technological development of computer science, the AI method-based MPPT methods are a new trend with fast tracking speed and convergence, and low oscillation [15]. * A lot of MPPT methods have been developed following soft computing techniques, including FLC, ANN, and ANFIS [47]. The drawbacks of these methods are that they need a large computer memory for training and the rule implementation. Figure 12: The output power of the solar panel at 25 \({}^{\circ}\)C with different solar radiation and the output power of the wind turbine (WT) with various wind speeds. * The next era of MPPT control is based on the evolution algorithms such as Genetic Algorithm, Cuckoo Search, Ant Colony Optimization, Bee Colony, Firefly Algorithms, and Random Search since these methods can efficiently solve the non-linear problems. 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. To overcome the disadvantages of these recent MPP methods, some researchers have focused on the field of Q-learning to handle the MPPT control problems. In reference [48], [PERSON] has developed Q-learning algorithm for MPPT control of variable-speed WT system, and [PERSON] applied the method for online MPPT control [17]. In addition, some researchers from National Chiayi University in Taiwan have proposed a RL-based MPPT method for the PV system [16]. One of the latest research examples in this area can be found in reference [18] where the authors proposed a new Q-learning based MPPT method for the PV system with larger state spaces, compared to only four states in reference [17] and reference [16]. The simulated results with good system performance from these papers show that the application of RL in the field of MPPT control is emerging and promising, which can help to improve the efficiency in renewable energy conversion, especially for solar and wind energy systems.