## 1 Introduction
In [9], the GMPP search process was based on the value of an intermediate parameter (termed as \"beta\"), which is calculated according to the PV modules open-circuit voltage and the number of PV modules connected in series in the PV string. All of the techniques described above cannot be applied to PV arrays with unknown operational characteristics. Therefore, they cannot be incorporated in commercial PV power converter products used in PV applications, where the operational characteristics of the PV array are determined by the end-user without having been known during the power converter manufacturing stage. In [10] a distributed maximum power point tracking (DMPPT) method is proposed, where the power converters of each PV module communicate with each other in order to detect deviations among the power levels generated by the individual PV modules of the PV array. That process enables the identification of the occurrence of partial shading conditions within the submodules that they comprise. The global MPP is then detected by searching with a conventional maximum power point tracking (MPPT) method (e.g., incremental-conductance) at particular voltage ranges derived similarly to the  
Figure 1: An example of the power-voltage characteristic of a photovoltaic (PV) array under partial shading conditions. \(0.8\times\mathrm{V_{oc}}\) GMPPT technique. Therefore, the implementation of this technique requires knowledge of the PV source arrangement/configuration and its operational characteristics. Evolutionary algorithms have also been applied, considering the GMPPT process as an optimization problem where the global optimum (i.e., GMPP) must be discovered so that the objective function corresponding to the output power of the PV array is maximized. The advantage of these types of algorithm is that they are capable of searching for the position of the GMPP on the power-voltage curve of the PV source with a lower number of search steps compared to the exhaustive scanning/sweeping process. These algorithms have been inspired by natural and biological processes and they differ on the operating principle used to produce the alternative operating points of the PV array, which are investigated during searching for the GMPP position. The particle swarm optimization (PSO) [11; 12; 13], grey wolf optimization (GWO) [14; 15; 16], flower pollination algorithm (FPA) [17; 18], Jaya algorithm [19] and differential evolution (DE) algorithm [20] are the most frequently employed evolutionary algorithms. Genetic algorithms (GAs) inspired by the process of natural evolution are applied in [21]. However, this type of optimization algorithm exhibits high computational complexity compared to other evolutionary algorithms, due to the complex operations (i.e., selection, mutation and crossover) which must be performed during the chromosomes search process. During operation, the evolutionary algorithms avoid convergence to local MPPs and converge to an operating point that is located close to the GMPP. Thus, they should be combined with a traditional MPPT method [e.g., perturbation and observation (P&O), incremental-conductance (INC) etc. ], which is executed after the evolution algorithm execution has been finished, in order to: (i) fine-tune the PV source operating point to the GMPP and (ii) maintain operation at the GMPP during short-term changes of solar irradiation or shading-pattern, which do not alter significantly the shape (e.g., relative position of the local and global MPPs) of the power-voltage curve (e.g., [15]). This enables to avoid frequent re-executions of the evolutionary algorithm, which would result in power loss due to operation away from the GMPP during the search process. The execution of the evolutionary algorithm is either re-initiated periodically (e.g., every few minutes), or after the detection of significant changes of the PV power production, in order to track any new position of the GMPP.