**6. Challenges and Future Work**

This paper comprehensively elaborates many recently reported works to track GMPP in PSCs in detail along with their pros and cons. Presently, over eighty MPPT optimization techniques have been published, and more than four new techniques are published each year. This article covers the recent findings in each MPPT technique in a tabular form. Because there are so many optimization strategies in the literature, picking one becomes quite challenging. Avoiding local MPP and local hotspots of PV array is critical for any optimization strategy. Moreover, when these algorithms are built, there is a requirement to manage energy. Research on efficient MPPT techniques can be rationalized in the future by considering many other critical factors such as local hotspots, array reconfigurations, and cell materials, which contribute to producing maximum power during PSCs. With the aid of smartphones, an MPPT application can also be set to work at any time via the Internet.

### **7. Conclusions**

Solar PV systems are regarded as the most capable energy source in renewable powergeneration systems due to the copious availability of sunlight. However, unpredictable weather makes their working efficiency low. Thus, MPPT techniques are used to yield maximum power from these systems in any weather conditions. Much research has been done till now in this field, but selecting an appropriate technique for specific circumstances has always been difficult. For the mentioned reason, this study reassesses the art of various MPPT optimization strategies developed by various researchers so far in a different manner. Conventional and AI-based MPPT techniques are elaborated separately with simplified flowcharts in respective sections with the aim to understand their basic principles in detail for new learners. Following the appropriate evaluation of each study, a tabular summary was created on important attributes of PV systems under PSCs, such as array size, % improvement in GMPP, level of irradiance, and tracking time, forming novel datasheets. In this paper, the reported taxonomy of MPPT techniques can help new learners, researchers, amd professional engineers to interpret the performance of each MPPT approach under different climatic scenarios. After careful analysis, it is easy to conclude that traditional techniques are less complex and work well in unshaded environmental conditions. However, they have the disadvantage of slow response. AI techniques perform well in PSCs with negligible oscillations in a steady state, with high accuracy and high tracking efficiency, but they suffer from high computational complexity. With the tabulated pros and cons of each reviewed article, new learners can easily find the research gaps that still exist in this field. With the help of the comparison table based on important parameters, while incorporating any MPPT in PV system, one can select most appropriate MPPT approach in a specific application. Furthermore, this analysis reveals that AI-based MPP controllers are the best option to deal with PSCs. As a result, a large research area has opened up for new researchers. To summarize, this review paper will be a useful resource for researchers or industrialists to utilize in choosing the most appropriate MPPT method for a certain objective.

**Author Contributions:** Conceptualization, A.K.S., R.K.P., S.C., A.F.M., M.A.A., H.M. and F.P.G.M.; Data curation, A.K.S., R.K.P., S.C., A.F.M., M.A.A., H.M. and F.P.G.M.; Formal analysis, A.K.S., R.K.P., S.C., A.F.M., M.A.A., H.M. and F.P.G.M.; Funding acquisition, H.M., M.A.A. and F.P.G.M.; Investigation, A.K.S., R.K.P., S.C., A.F.M., M.A.A., H.M. and F.P.G.M.; Methodology, A.K.S., R.K.P., S.C., A.F.M., M.A.A., H.M. and F.P.G.M.; Project administration, H.M., M.A.A. and F.P.G.M.; Resources, H.M., M.A.A. and F.P.G.M.; Software, H.M., M.A.A. and F.P.G.M.; Supervision, H.M., M.A.A., R.K.P., S.C. and F.P.G.M.; Validation, A.K.S., R.K.P., S.C., A.F.M., M.A.A., H.M. and F.P.G.M.; Visualization, A.K.S., R.K.P., S.C., A.F.M., M.A.A., H.M. and F.P.G.M.; Writing—original draft, A.K.S., R.K.P., S.C., A.F.M., M.A.A., H.M. and F.P.G.M.; Writing—review & editing, A.K.S., R.K.P., S.C., A.F.M., M.A.A., H.M. and F.P.G.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work reported here in has been financially supported by the Intelligent Prognostic Private Limited Delhi, India under Research Grant XX-02/2022.

**Data Availability Statement:** Data will be provided on request.

**Acknowledgments:** This study was supported by the Universiti Teknologi Malaysia—"Development of Adaptive and Predictive ACMV/HVAC Health Monitoring System Using IoT, Advanced FDD, and Weather Forecast Algorithms" (Q.J130000.3823.31J06). The authors would like to acknowledge the support from Ingenium Research Group, Universidad Castilla-La Mancha, 13071 Ciudad Real, Spain, support from Integral University, Lucknow, support from Universiti Teknologi Malaysia (UTM), and support from Intelligent Prognostic Private Limited Delhi, India researcher's supporting Project.

**Conflicts of Interest:** The authors declare no conflict of interest.
