# Evolutionary Maximization of Energy Amount Harvested by Means of Panel of Thermoelectric Modules
## 3 Thermoelectric Panel with Evolutionary MPPT  
As was pointed out in the previous section, the presence of internal resistances of TE modules causes undesired energy loss during the conversion process. This Joule heat dissipated on devices may be minimized by match optimization of electric load to source of TE modules connected in series in local groups. For example in [13; 14; 23; 24] the descriptions of perturbation and observation, increment conductance, constant voltage and fractional open-circuit voltage MPPT methods are presented. However, papers [25; 26; 27; 28] propose alternative MPPT techniques by means of artificial computational intelligence methods.  
The solution proposed in this work is based on evolutionary computations, which imitate natural processes of rivalry in population of individuals [29]. This optimization methods may be used to solve many kinds of difficult tasks, and exemplary applications are: analog electronic circuits testing and diagnosing with elements' tolerance dispersion presence [30; 31] or image processing with pixel noise presence [32]. These referenced papers propose the usage of different kinds of evolutionary algorithm (EA) but five common stages  
Figure 1: Thermoelectric module structure.  
of evolutionary computations are: individuals (chromosomes or genotypes) quality (fitness) determination, mating (reproducing) pool selection with assumed pressure, genetic material (code created from alleles) recombination, random mutation and succession of offspring generation. The fundamental issue of EA application to the considered task is adequate designing of code structure of individuals and respective formulation of fitness function and genetic operations. They should guarantee the desired speed of heuristic probing of searching space. All the elements of EA application to MPPT mentioned here are described in detail in the next subsection, however the idea of the proposed MPPs evolutionary tracking in serially connected local strings of thermoelectric modules is illustrated in Figure 2. This is an example panel which consists of four sections (\(N=4\)) of modules controlled individually and the proposed idea can be easily extended to larger installations. Each local string consists of a dedicated DC/DC simple converter with regulated voltage gain that makes it possible to track the optimal operational point of individual string of TEGs. Control signals \(x_{c1}(t)\),..., \(x_{cN}(t)\) decide about conversion points of DC/DC block in the dedicated section and they are square waveforms with duty cycles determined evolutionarily to levels allowing harvest of the maximal electric power from specific string of panel modules. These signals decide the voltage gains of particular boost converters and allow to adapt them to the current conditions of system electric load and thermal profile present on the panel. The evolutionary optimization proceeds among local devices and this makes it possible to effectively investigate the optimal operating point of each group and results in the total electric energy produced maximization. Besides, the proposed multi-strings/multi-converters dispersed structure from Figure 2 assures relatively small levels of electric currents of local low power boost converters and this should proceed with higher total efficiency than for a single common converter processing the whole power of the panel.