# Evolutionary Maximization of Energy Amount Harvested by Means of Panel of Thermoelectric Modules
## 5 Practical Experiment
The evolutionary MPPT results may be observed in Figure 10. There are records of measurements of the temperatures: on the hot side \(T_{H}\), the cold side \(T_{C}\) and their difference value \(\Delta T\) as well as voltage on boost converter output with the evolutionary MPPT optimization up to 1 min (\(x_{c1}(t)\) PWM regulated) and without it after this time  
Figure 9: The power matching curve measured for the tested TE panel underside. (\(x_{c1}(t)\) zeroed, continuous oscillations of converter). These measurements are achieved for a steady state of thermal transfer in the tested TE system. The temperatures on the hot and cold sides of TE devices have relatively low variations and they fluctuate in narrow ranges (67 \({}^{\circ}\)C, 72 \({}^{\circ}\)C) and (56 \({}^{\circ}\)C, 59 \({}^{\circ}\)C) approximately. The mean value of temperature difference \(\Delta T\) is close to 12 \({}^{\circ}\)C.  
The measurements (a),..., (d) of the above sets of figures were obtained for the resistive loads \(R_{o}\) = 50 \(\Omega\), \(R_{o}\) = 100 \(\Omega\), \(R_{o}\) = 200 \(\Omega\), \(R_{o}\) = 500 \(\Omega\), respectively. As was expected, the load voltage \(U_{out}\) is higher when MPP is tracked evolutionarily (i.e., for the first 1 min of this experiment). This procedure allows to maximize the TE panel total efficiency and the MPP may be investigated precisely in individual string sections by means of single low power microcontroller system and effective tracking algorithm. It is worth noticing in Figure 10 that the temperatures difference \(\Delta T\) (approximated curve) decreases when MPPT algorithm is disabled (i.e., always after 1 min from beginning the experiment). This specific effect proved conversion efficiency improvement assured by the proposed algorithm, i.e., Joule heat energy lost on the inner resistance of TE is higher for non-optimized stages and this causes \(T_{H}\)-\(T_{C}\) gradient degradation. Besides, an evolutionary searching is active all the time and results in local deviations of resultant voltage \(U_{out}\), however its average level is near to the optimal one. This local probing of MPP search space (duty cycles regulations in the area defined by \(C_{MX}\)) guarantees improved local tracking as well as expected reactions  
Figure 10: The temperatures and DC/DC converter output voltage curves up to minute obtained with MPPT stopped after 1 min for loads: (**a**) \(R_{o}\) = 50 \(\Omega\), (**b**) \(R_{o}\) = 100 \(\Omega\), (**c**) \(R_{o}\) = 200 \(\Omega\), (**d**) \(R_{o}\) = 500 \(\Omega\). in case of load points changes. In effect, the optimal thermal to electric energy operational point of transformation is rapidly corrected if necessary. The electrical powers achieved for evolutionarily optimized MPP as well as the ones for boost continuous conversion are collected in Figure 6:0 The experiment results prove that the proposed MPPT algorithm assures significant minimization of loss of energy dissipated on the internal parasitic resistances of thermoelements. For the two out of four cases considered here, the power produced after optimization (the PWM control activated) is twice higher than without it (i.e., for converter continuous oscillations). Of course, the range of optimal point tracking is limited by performances of simple boost converter used and for \(R_{o}\) = 500 \(\Omega\) the power produced progress is close to 25%. However, the final range of effective MPTT regulation seems to be still acceptable for the proposed low cost solution and this allows to save the significant part of energy inputted to process. It has to be emphasized that energetic yield which occurred by the proposed evolutionary optimization should be even bigger for numerous sections of the thermovoltaic panel with strings managed by individual DC/DC converters.