## 1 Introduction  
Global energy consumption has increased noticeably due to the increase in the population. The sustainable use of renewable energy solves one of the major concerns of the world community, since the amount of fossil energy sources is no longer sufficient [1]. Energy generated from photovoltaic (PV) systems is inexhaustible; hence, PV systems are suitable candidates for a long-term, reliable, and environmentally friendly source of electricity [2]. The output power of a PV systems varies with the intensity of the solar irradiance and the environmental temperature. Due to the nonlinear current-voltage (_I-V_) characteristics of the solar cell, there is a unique maximum power point (MPP) on the power-voltage (_P-V_) curve. Global maximum power point tracking (GMPPT) is one of the basic measures to improve the overall efficiency of the PV power generation systems. However, real environments experience partial shading, which is a condition whereby a portion of the PV module (or array) is shaded, while the other parts remain uniformly irradiated. When a PV array is subjected to partial shading, a considerable amount of energy is lost because the shaded module is short-circuited by its bypass diode. This forces its voltage, and consequently power, to be reduced to zero and the \(P\)-\(V\) curve exhibits a complex multi-peak shape. In this case, the traditional algorithms using gradient information, such as the perturb-and-observe (P & O) and incremental conductance (INC) algorithms, become invalid [3; 4; 5; 6; 7; 8]. In the last decade, several researchers have compared various MPPT techniques [9; 10], focusing on the \(P\)-\(V\) characteristics [11; 12; 13; 14; 15], models [16; 17; 18; 19], and methods [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33] to track the maximum power of PV modules/arrays under partial shading conditions (PSC). The research on the PV output characteristics is mainly focused on the analysis of failure, power loss, and voltage variations in the MPPT method under the PSC. The research on the PV model mainly concentrates on a unified model of the PV array and an accurate model of the PV unit under complex environments. In general, the results of the studies on PV output characteristics and models are often fed back for updating the GMPPT algorithm; thus, they are significant for the applications of the PV system. The maximum power tracking algorithms, which are the core of the PV power generation systems, can be broadly divided into three categories:  
In the first category, the GMPP can be obtained by traversing all the maximum points in the PV array, which are the local maximum power points (LMPPs), and then sorting this maximum group [20; 21; 22]. The advantage is that the GMPP can be found accurately, and the algorithm is simple and stable; however, the optimization process is slow, redundant, and wastes hardware and software resources. In the second category, the region of maximum power is determined according to the PV and model characteristics, and the multi-peak problem is transformed into a unimodal problem of the region. Then, the traditional method is used to track the MPP [23; 24; 25; 26]. The advantage of the methods in this category is that they can reduce the traversal region; however, they have a higher dependency on the analysis of the local shadow models and output characteristics, which means that the determination of the MPP region is difficult and the robustness is poor. In the third category, using a heuristic and intelligent optimization algorithm, the global maximum power of the PV system is directly calculated using an iterative method [27; 28; 29; 30; 31; 32; 33], to achieve MPPT. The advantages of these methods are that they do not depend on the model or PV characteristics, and can even update the algorithm through online follow-ups and self-learning.