Comprehensive Review of Conventional and Emerging Maximum Power Point Tracking Algorithms for Uniformly and Partially Shaded Solar Photovoltaic Systems

Abstract

The authors would like to thank "Universidad Panamericana" for their support through the project "Estudio de topologías de convertidores de cd-cd" grand number: UP-CI-2022-GDL-06-ING."ABSTRACT Renewable energy utilization is the only suitable solution to diminish the increasing level of greenhouse gas emissions, fuel costs, and energy crisis in the next generation. Out of many renewable sources, solar energy sources that are clean, green, and emission-free have gained wide utilization despite their intermittency nature. Several solar photovoltaic (PV) panels are connected in series-parallel to achieve the energy demand. In such a system, it is possible that each panel operates differently due to uneven temperature and irradiation that results in uniform and partial shading conditions. Thus, a unique and efficient mechanism is required to extract maximum power from uniformly and partially shaded PV systems. Numerous maximum power point tracking (MPPT) methods have been developed to increase the efficiency and lifetime of photovoltaic systems. This study provides a unique, in-depth, and organized review of MPPT methods under four categories: classical, intelligent, optimization, and hybrid techniques. All possible selection benchmarks are considered to do a comprehensive review, which is not deliberated in the existing review literature. Based on the selection benchmarks, the advantages and disadvantages of each MPPT technique under different categories are summarized in tabulated form. To address the research gaps for further investigation in this field, a concise discussion is included at the end. This review article may find an accessible reference for engineers to understand the most useful MPPT method and to undertake extensive research in PV systems.

Comparison of Bioinspired Techniques for Tracking Maximum Power under Variable Environmental Conditions

“…PSO [5,6,8,11,13,14,16,17,19,[23][24][25][26], GA [8,[11][12][13][14]23], and ABC [5,8,[11][12][13][16][17][18][19] are the most widely used optimization algorithms because of some advantages [5,16,17] such as their simple structure, independence of a mathematical model, capability to solve nonlinear and challenging mathematical problems, easy understanding, and high reliability. Each optimization technique considers a few deciding factors such as the number of populations and a few particles/food sources/chromosomes/jellyfsh.…”

“…Te method is repeated until the requirements are met (P max ). Finally, the overview of the genetic algorithm's [8,12,13,23] working principle is displayed in Figure 4(a) [13].…”

“…Diferent users have used diferent parameters and specifcations to prove that their technique is better than the existing one, so diferent comparison parameters are considered such as tracking time [16], RMSE [14,15,18], efciency [13,17,22,24,27,29,31,[34][35][36], MAE [20], accuracy [41], reduced rise time [21], settling time/ tracking time [21,29,35], and maximum power [28,35,36]. In this paper, maximum power tracked, maximum efciency, total execution time, convergence speed, complexity, parameter, steady-state oscillation, stability, sensitivity, tracking ability, and economy have been compared [10,12,13,23,27]; FPA performs better with a small margin than PSO in terms of maximum power [28]. In comparison to a recent 2023 research article [13], M-PSO has the highest efciency of 99.87, [22] RAS 99.85%, and [29] PSO-BOA 99.7% with 0.47 sec tracking time.…”

“…A few of the well-known optimization techniques are artifcial immune system (AIS) [8], ant colony optimization (ACO) [5,8,[12][13][14], moth-fame optimization (MFO) [12,15], whale optimization algorithm (WOA) [12], butterfy optimization (BFO) algorithm [12], shufed frog leaping algorithms (SFLA) [12], slap swarm algorithm [12], frefy algorithm (FA) [12,16,17], artifcial bee colony (ABC) [5,8,9,12,13,[16][17][18][19][20], bat-search algorithm (BA) [12,17], grey wolf optimization (GWO) [5,12,13,15,17,21,22], genetic algorithm (GA) [8, 12-14, 21, 23], fower pollination optimization (FPO) [12,16,24], JAYA [16], DE [8,16], cuckoo search (CS) [5,6,12,13,15,16,…”

“…[37] gives the modeling [38], electrical characteristics, and [39] parameter estimation of photovoltaic strings under PSC and explains the importance of bypass diodes Te authors in [15,19,[40][41][42] has reviewed and compared the performance of some well-known optimization algorithms under partially shaded conditions. Te authors in [13] have reviewed and given the performance comparisons of conventional, artifcial intelligence, and optimization-based MPPT in terms of tracking capability, convergence, implementation, tracking accuracy, tracking speed, efciency, economy, and application and have given advantages and disadvantages of diferent optimization techniques used to track the GMPP. In [43], a comprehensive comparative analysis of the twenty-eight adaptive MPPT algorithms is performed based on the tracking time, steady-state oscillations, conversion efciency, algorithm complexity, implementation cost, and the capability to perform on the partial shading conditions.…”