# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 6 The Latest Research on MPPT in PV  
Apart from its application, PV are expected to be operated in a manner such that maximum power can be extracted from the installed system. The energy output of a PV system is sensitive to variations in weather conditions; in particular, it is dependent on solar radiation and temperature. Variations in cloud cover, fog and heat affect the PV system's conversion efficiency. Dust and other particles floating in the air or covering the panel can drastically decrease the efficiency of the power conversion process as well [76]. Under these conditions, the power-voltage curve of the PV array exhibits multiple local maximum power points (MPPs). However, only one of these MPPs corresponds to the global MPP (GMPP), where the PV array produces the maximum total power [77]. (Figure 2). Any change in the output voltage because of the change of load or other reasons will cause the PV panel to produce less power than the maximum. Therefore, the controller of the power converter that is connected at the output of the PV array must execute an effective global MPP tracking (GMPPT) process to continuously operate the PV array at the GMPP during continuously changing weather conditions. Consequently, many research efforts are focused on finding ways to drive PV panels to their maximum output power at all weather conditions, thus ensuring their profitability [78]. In Table 6 a list of papers that provide a review on PV MPPT techniques is shown. \begin{table}
\begin{tabular}{p{42.7 pt} p{34.1 pt} p{34.1 pt}} \hline \hline
**Year** & **Reference** & **Notes** \\ \hline
2021 & [79] & The paper provides a comparative and comprehensive review of some relevant PSO-based methods taking into account the effects of important key issues such as particles initialization criteria, search space, convergence speed, initial parameters, performance with and without partial shading and efficiency. \\ \hline
2021 & [80] & The paper intends to review the previous articles and provide a proper division, performance method. This explains the performance, application, advantages and disadvantages of algorithms to be a good reference for selecting the appropriate algorithm. Algorithms in the presented paper are divided into four categories methods based on measurement, calculation, intelligent schemes and hybrid schemes. \\ \hline
2021 & [81] & The paper represents a review of two modern techniques used in solar photovoltaic systems which enhance the extraction of maximum output power in an efficient manner. The Artificial Intelligence-Based MPPT Techniques for PV Applications and a Forecasting System of Solar PV Power Generation using Wavelet Decomposition and Bias- compensated RF are reviewed and compared in the paper. \\ \hline
2021 & [82] & The paper presents an organized and concise review of MPPT techniques implemented for the PV systems in literature along with recent publications on various hardware design methodologies. Their classification is done into four categories, i.e., classical, intelligent, optimal and hybrid depending on the tracking algorithm utilized to track MPP under PSCs. \\ \hline
2021 & [83] & The review of MPPT techniques proposed in the paper has been grouped into two groups. The first group includes all the benchmark facilities. The second group includes the intelligent techniques that explain the fuzzy-based MPPT, ANN-based MPPT evolutionary techniques, hybrid methods and MPPT techniques used in energy harvesting. \\ \hline
2020 & [84] & In the presented paper, a compendious study of different Swarm Intelligence (SJ)-based MPPT algorithms for PV systems feasible under partially shaded conditions are presented. The methods are compared in terms of their swarm intelligence and advantages. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Review papers on PV MPPT techniques. Publication year considered: 2018–2021. Figure 2: An example of the power-voltage characteristic of a photovoltaic (PV) array under partial shading conditions [77].