# Design of Direct Current Microgrid Converter with Cost-Effective Low-Voltage Battery Storage System
## 9 Experimental Results
### Maximum Power Point Tracking Verification  
To verify the maximum operating point tracking function on a prototype, a power source must be connected to the charging inverter input since the use of a solar cell would be highly problematic due to the need for a constant luminous flux, due to the maintenance of the cell parameters for the measurement period of the charging inverter and equally for the measurement period of the cell parameters themselves. Artificial lighting and  
Figure 16: Function prototype of the designed system: (**a**) 3D model and (**b**) real system.  
Figure 15: Simulation result of the balancing circuit.  
maintaining the same conditions would be required. We will, therefore, use the alternative method described in Figure 17, where a voltage source with a sufficient current range is artificially supplied with a Ri resistor of known value, preferably a power resistor with a small temperature coefficient. Once these conditions are met, we can consider the resistor Ri = 6.8 \(\Omega\) as a constant; in the case of a classical laboratory-regulated DC source, because of the introduction of feedback in the source itself, we can consider its internal resistance to be almost zero and thus negligible concerning the value of Ri.  
To achieve the maximum possible power transfer, the charging inverter should adjust its imaginary internal resistance R\({}_{\rm x}\), neglecting the resistance of the wires and ammeter, R\({}_{\rm i}\) = R\({}_{\rm x}\).  
For this ideal case, we can then calculate and plot the P-A and V-A characteristics Figure 18, to which we mark the peak of the power curve and, hence, the maximum power point. The shape of the curve is, of course, different than the shape of the curve for the solar cell (Figure 5, but from the principle of the function of the algorithms for finding the maximum operating point, changing the shape of the curve will not affect the overall results, provided that the curve contains just one maximum.  
For the other parameters, we can then generally write the following:  
\[I_{mpp}=\frac{U_{0}}{R_{i}+R_{x}}=\frac{24}{3.429+3.429}=3.499\ \rm{A} \tag{8}\]  
\[V_{mpp}=U_{0}-(R_{i}\cdot I_{mpp})=24-(3.429\cdot 3.499)=12\ \rm{V} \tag{9}\]  
\[P_{mpp}=U_{mpp}\cdot I_{mpp}=12\cdot 3.499=41.994\ \rm{W} \tag{10}\]  
Figure 17: Schematic connection for MPPT function verification.  
Figure 18: PA and VA characteristics of the replacement model.  
Thus, we will test the algorithm at three points for three different known values of \(\rm R_{i}\). We then compare the measured results and the theoretically calculated values of the possible power maximum by percentage to obtain the deviation from the ideal state (see Table 4).  
Table 4 shows that the inverter's operating steady-state point is within 4% of the ideal point of maximum output. This error can be caused by several factors, such as neglecting the ammeter's internal resistance, the resistance of the lead wires, measurement calibration, or voltage ripple.  
Figure 19 graphically shows the measurement results and the positions of the actual measured PowerPoints concerning the ideal curve. It can be observed that the point locations are similar for all three power curves. Thus, the error is applied equally to all tested powers. Given a considered solar cell power of 50 W, which produces a power loss of 2 W at a maximum point error of 4% and a measured mean inverter efficiency of 93%, we can estimate the resulting efficiency of the charging inverter with MPPT function to be 89.2% at the maximum considered power of 50 W.