*5.1. Start-Up Test*

The first step to simulate the PSC is to set the solar irradiation of three PV strings to be 200 <sup>W</sup>/m2, 300 <sup>W</sup>/m2, and 1000 <sup>W</sup>/m2, respectively. The online optimization responses of various methods for MPPT are illustrated in Figure 5. It is clear that INC can easily reach the point of steady convergence in far less time than the other methods. However, it has a vital drawback in that it cannot make an effective distinction between GMPP and LMPP, which means it might often be trapped at a low-quality local optimum as it is readily stagnated at an MPP. Generally speaking, due to their significant ability of global searching, other meta-heuristic algorithms can usually find a better quality optimum with larger power and energy. Among them, TRL owns the highest convergence stability as it can avoid a blind/random search by the use of knowledge transfer.

**Figure 5.** PV system responses of seven methods obtained on the start-up test. (**a**) Voltage; (**b**) Power.

### *5.2. Step Change in Solar Irradiation with Constant Temperature*

As shown in Figure 6, the core process is to impose a set of solar irradiation steps on the PV array, where the step change is applied every second. The temperature is maintained to be constant at 25 ◦C during the whole test. The online optimization outcomes of various approaches for MPPT with step change solar irradiations are illustrated in Figure 7. It can be found that the obtained results are similar to those of the start-up test. The output power and voltage derived by those meta-heuristic algorithms, except TRL, are relatively prone to volatility if the solar irradiation is not always steady and varies at a dramatic pace. This also verifies that the knowledge transfer can effectively guarantee the convergence stability of TRL, i.e., the control strategies of adjacent optimization tasks only have a slight difference under the same weather conditions.

**Figure 6.** Step change of solar irradiation with PSC. PSC: Partial Shading Conditions.

**Figure 7.** PV system responses of seven methods obtained on the step change in solar irradiation with constant temperature. (**a**) Voltage; (**b**) Power.

### *5.3. Gradual Change in Both Solar Irradiation and Temperature*

Figures 8 and 9 show the procured results of seven algorithms for MPPT when solar irradiation and temperature both change gradually. A conclusion can be drawn that, except for TRL, the other meta-heuristic algorithms are still prone to generating the larger power fluctuations, even when the solar irradiation and temperature change slowly. Due to the beneficial guidance by knowledge transfer, TRL can significantly alleviate the power fluctuations without a blind/random search.

This also reveals that, for real-time MPPT, TRL is capable of speedily seeking an optimum of high quality through the space decomposition on the basis of RL and beneficial knowledge transfer.

 **Figure 8.** Gradual change in both solar irradiation and temperature. (**a**) Irradiation and (**b**) temperature.

 **Figure 9.** *Cont*.

**Figure 9.** PV system responses of seven methods obtained on the gradual change in both solar irradiation and temperature. (**a**) Voltage; (**b**) Power.

### *5.4. Daily Field Profile of Solar Irradiation and Temperature in Hong Kong*

For the purpose of testing the specific practicability of TRL in practical application, the temperature and solar irradiation measured in Hong Kong was used to simulate the PV system for MPPT (See Figures 10 and 11). The metrical data are mainly selected from four representative days of four different seasons in 2016, in which the interval of data is set to 10 min. Note that the randomness and intermittence of solar energy and renewable energy system (RES) [34–37] is a very common issue usually resulting from uncertain atmospheric conditions.

 **Figure 10.** Daily profile of solar irradiation and temperature in Hong Kong. (**a**) Irradiation; (**b**) Temperature.

**Figure 11.** The detailed geographical position of the measuring device for solar irradiation and temperature.

Figures 12 and 13 demonstrate the output power of seven algorithms for MPPT in different seasons. It can be well illustrated that, compared with INC, in the PV system, all the meta-heuristic algorithms can obtain more output power, where the output energy of TRL reaches 115.52% of that of INC in the spring. That aside, one can derive that although the performances of all meta-heuristic algorithms are comparatively small during the whole simulation period, TRL can still outperform other algorithms, which means that it can always give out the most power in any season.

**Figure 12.** PV system responses obtained on a typical day in Hong Kong. (**a**) Spring; (**b**) summer; (**c**) autumn; (**d**) winter.

**Figure 13.** Statistical results of output energy of the PV system obtained by seven algorithms in different seasons.
