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A new age of renewable energy has been started with a massive welcome all over the world. Kapadia et al. (2019), in their review, stated that tidal, bio-Gas, wind, and solar power became essential sources of generation in recent years. Curto (2018) proposed a sea wave source for the generation of electricity. As per Hodge et al. (2018), the PV system is 2nd most utilized renewable energy source among all renewable sources after wind. As per Kabir et al. (2018), with the advancement in technology, the size and performance of the system have increased to a great extent which has reduced the cost of the PV system to a greater extent. Kannan & Vakeesan (2016) revealed in their review that solar power has advantages over other renewable energy sources in terms of location, price, availability, capacity, and productivity. An MPPT and a DC-DC converter are connected between the solar module and the load to extract the maximum power from the PV module. Though many MPPT techniques are available in the literature, different methods and module specifications were used in each article, so it is not easy to compare them as per their performance. According to Basha & Rani (2020), MPPT should draw the maximum power in less time to prove the better efficiency of the system. According to Pakkiraiah (2016), there are many conventional and evolutionary MPPT approaches based on the duty cycle control mechanism and challenges in implementing MPPT. Conventional-based MPPT Techniques have the advantage, they can be used for low and high-power applications. Still, frequent or continuous changes in irradiance and temperature result in less conversion efficiency as the MPPT charge controller often fails to detect the Maximum point. Chen et al. (2017) has analyzed the problems that are taking place in MPPT and suggested the solutions to overcome them, Artificial Intelligent techniques are less dependent on the model parameters and have a self-learning approach, which acts as an advantage over conventional. In hybrid-based MPPT, the benefit of conventional techniques and AI-based methods are combined to obtain better output.
The DC-DC converter is used to boost the module output; it is a combination of Inductor, Capacitor, diode, and Power MOSFET/IGBT devices, whose Gate Pulse is controlled by using the MPPT techniques. Basha (2020) & Pakkiraiah (2016) had done a comprehensive analysis and survey on conventional and AI-based MPPT, which can improve the efficiency of a PV system. Few researchers have implemented P&O (Salman et al.,2018), InC (Elgendy et al.,2013), MIC (Tey & Mekhilef,2014; Saad et al.,2018), FLC (Soufi et al., 2014; Sadeq et al.,2018; Kapumpa & Chouhan,2017), ANN (Agha et al.,2017; Ferrero et al.,2019; Manas et al.,2016), NFN (Bendib et al.,2014; Hameed et al.,2019; Subiyanto et al.,2012) and ANFIS (Haji & Naci,2020; Sheik Mohammed et al.,2016; Aldair et al.,2018; Iqbal et al.,2017; Lotfi Farah et al.,2020) using different converters. Jain et al. (2018) has done a comparative analysis on P&O and InC based MPPT. Selman & Nasir (2016) and D.Yadav et al. (2021) compared P&O, InC, and FLC in terms of their response on resistive load. Jain et al. (2014) compared FLC, P&O, and InC techniques at different weather conditions. Soteris & Arzu. (2010) discussed Artificial Intelligence Techniques in solar energy applications on solar collectors and Panels. V Mahajan et al. (2014) has given AI application, and Vacheva et al. (2019) has given different Application of ANN for Converter section. A systematic review of the most commonly used methods is given by Ferrero et al. (2019) regarding various applications. Lotfi Farah et al. (2020) compared FLC and ANFIS in terms of performance.