Phát triển thuật toán xác định điểm tối ưu toàn cục của pin mặt trời trong điều kiện chiếu sáng không đồng nhất trên bề mặt

of DC 
boost converter is presented in Figure 3. 
 PV String
 + 
 RL Figure 4. P - V characteristic of two tested states 
 - Figure 5 and 6 present the generated power and 
 voltage with the applications of the two different MPPT 
 GA algorithms under the uniform irradiance in State 1. 
 According to these two figures, the generated power in the 
 identify state of both algorithms are similar, at 1300W, 
 because the irradiance intensities among the solar PV 
 Figure 3. Simulation diagram with PSIM panels are not largely different and the P&O algorithm start 
 The panels which are used in the simulation model, are tracking from the right-hand side. The proposed algorithm 
based on the Green Wing module GW - BD16/72, with the requires 24 times of changing positions (eight calculation 
max power of 310W and the parameters under test cycle) to get the convergence, meanwhile the P&O 
conditions as follows: Battery type: monocrystalline (Mono). algorithm requires only 3 times of irradiance change for the 
Numbers of photovoltaic cells in one module: 72. Voltage convergence. 
 Figure 7 and 8 present the generated power and 
at MPP: VMPP = 38.2V. Current at MPPT: IMPP = 8.9A. Open 
circuit voltage: 46.2V. Short circuit current: 9.5A. Heat voltage with the applications of the two different MPPT 
 o algorithms under the uniform irradiance in State 2. 
coefficient according to Voc: -0.29%/ C 
 Website: https://tapchikhcn.haui.edu.vn Vol. 56 - No. 6 (Dec 2020) ● Journal of SCIENCE & TECHNOLOGY 21
 KHOA H ỌC CÔNG NGHỆ P - ISSN 1859 - 3585 E - ISSN 2615 - 961 9 
 P&O GA P&O GA
 1400 600
 1200
 500
 1000
 400
 800
 300
 600
 400 200
 200 100
 0
 0
 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
 Time (s) 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
 Time (s) 
 Figure 5. Power of state 1 Figure 8. Voltage of state 2 
 P&O GA
 P&O GA
 1000
 1400
 800 1200
 1000
 600
 800
 400 600
 400
 200
 200
 0 0
 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0 0.1 0.2 0.3 0.4
 Time (s) Time (s) 
 Figure 6. Voltage of state 1 Figure 9. Power of increased irradiance with proposed algorithm 
 In this experiment, there is a difference in generated P&O GA
 power with the applications of the two algorithms. The 800
 P&O algorithm is trapped in the local maximum power 
 600
 point, with a power difference of 100W compared to the 
 maximum power of 700W. Meanwhile, the proposed 400
 algorithm can correctly detect the GMPP. The convergence 
 time of the P&O algorithm is slower than that of State 1, at 200
 one cycle. The convergence time of the proposed 
 algorithm does not change, compared to that of State 1. 0
 0 0.1 0.2 0.3 0.4
 In the two cases of irradiance change, the setting of the Time (s) 
 changing time is 0.2s. The experimental results of the Figure 10. Power of increased irradiance with P&O 
 irradiance increase cases with two investigated algorithms P&O GA
 are shown in Figure 9 and 10. The process of starting the 1400
 system within the first 0.2s is the same as those analysed in 1200
 the experiment of the State 2 with uniform irradiance. After 1000
 changing the irradiance, the generated power with the 800
 application of the proposed algorithm is still the same as 600
 those of the State 1 with uniform irradiance. However, with 400
 the application of the P&O algorithm, the generated power 200
 is 250W lower than the maximum power of State 1 with 0
 uniform irradiance. At the same time, the time for MPPT 0 0.1 0.2 0.3 0.4
 Time (s) 
 tracking is longer. 
 Figure 11. Power of decreased irradiance with proposed algorithm 
 P&O GA
 P&O GA
 1000
 1000
 800
 800
 600
 600
 400
 400
 200 200
 0 0
 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0 0.1 0.2 0.3 0.4
 Time (s) Time (s) 
 Figure 7. Power of state 2 Figure 12. Power of decreased irradiance with P&O 
22 Tạp chí KHOA HỌC VÀ CÔNG NGHỆ ● Tập 56 - Số 6 (12/2020) Website: https://tapchikhcn.haui.edu.vn 
P-ISSN 1859-3585 E-ISSN 2615-9619 SCIENCE - TECHNOLOGY 
 The experimental results of irradiance decrease with the 
application of the two investigated algorithms are 
presented in Figure 11, 12. In both states, the P&O 
algorithm is trapped into the local maximum power points. 
 In the first case, the power reduces by 10%, at 130W, 
and in the second case, the power reduces by 20%, at 
160W. Time for tracking the MPPs are the same for all 
experiments, because the algorithm is independent from 
the gap between the initial point and the maximum point. 
In these experiments, the tracking time with the 
application of P&O algorithm is the longest (5 cycles - 0.1s). 
 P&O GA
 1400
 1200
 Figure 15. Chroma Array Simulator Interface 
 1000
 800 3.3.2. DC - DC conversion circuit 
 600 A DC - DC voltage conversion circuit according to the 
 400 principle of the boost circuit has been constructed with the 
 200 circuit diagram as shown in Figure 16. In addition to the DC 
 0 - DC boost circuit principle, the experimental circuit uses a 
 0 0.1 0.2 0.3 0.4 0.5 0.6 voltage divider and a shunt resistor to obtain the voltage 
 Time (s) 
 and current measurement signals. The circuit parameters 
 Figure 13. Power of irradiance change for a long period 
 are given as follows: Permissible input voltage: 80V; 
 Vo1 Vo2
 Permissible output voltage: 200V; Rated capacity: 500W; 
 1000
 Shunt resistance: 0.05Ω. 
 800 The controlling circuit in the article (Figure 17) uses the 
 600 Arduino Uno microprocessor as the central controller, 
 400 which is responsible for receiving analog signals, 
 calculating the MPPT algorithm and the PWM that control 
 200 the MOSFETs respectively. The voltage reading pins of 
 0 Arduino are taken directly from the dynamic circuit, and the 
 0 0.1 0.2 0.3 0.4 0.5 0.6 current reading pins are taken from the current signal 
 Time (s) 
 amplified by the opto amp amplifier. PWM signals which 
 Figure 14. Voltage of irradiance change for a long period 
 are taken from Arduino, do not have sufficiently minimum 
3.3. Experiment model voltage to excite the MOSFET (10V), so the paper uses the 
 3.3.1. Chroma solar PV simulation TLP250 optical opto dedicated to excite the MOSFET. The 
 The Chroma Solar experimental model can easily set up supply power for the controlling circuit is taken from the 
 grid through the adapter, providing the voltage of 9V for 
the VOC, ISC, Vmp, Imp parameters to simulate the typical 
output of solar PV cell at fast and stable response time. It Arduino and 15V for the MOSFET switching excitation 
can communicate with peripheral devices through circuit (Figure 16). 
connection ports such as Internet, USB, RS-485, RS232, etc. 
 It is easy to use the software with an intuitive interface 
(Figure 15). The I-V and P-V characteristic curves can be 
easily programmed for real-time testing. It also displays 
MPPT status for PV inverter. The functions of reporting and 
real-time monitoring are fully displayed on the screen. The 
time for testing the characteristic curves should be set 
between 60 and 600 seconds in order to analyse the MPPT 
efficiency at best. A built-in I-V characteristic in the software 
allows us to enter the data on the desired maximum input 
 Figure 16. Diagram of experimental dynamic circuit 
power Pmax, Vmin, Vnom, Vmax to test the PV inverter. We can 
directly enter the percentage value of the desired 3.3.3. Controlling circuit 
maximum power (5%, 10%, 20%, 25%,, 50%, 75%, 100%) Diagram of experimental Controlling circuit as shown in 
and the software will automatically generate the I-V Figure 17. The components of the designed circuit are 
characteristic curve of the experimented solar PV cell. presented in Table 2. 
 Website: https://tapchikhcn.haui.edu.vn Vol. 56 - No. 6 (Dec 2020) ● Journal of SCIENCE & TECHNOLOGY 23
 KHOA H ỌC CÔNG NGHỆ P - ISSN 1859 - 3585 E - ISSN 2615 - 961 9 
 conditions and studied the energy efficiency obtained from 
 the system. Similar to the simulation, the real experiment is 
 also based on two irradiance states with small difference in 
 the irradiance (case 1) and large difference (case 2). The 
 obtained results after completing the MPPT detection are 
 shown in Figure 19, 20. The establishment time in both 
 cases is similar (4s) and the establishment errors of each 
 case is 0.4% and 0.7%, respectively. 
 Figure 17. Diagram of controlling circuit 
 Table 2. Component parameters of the designed circuit 
 Components Parameters 
 Dynamic circuit 
 Input conductor Cin 220µF - 100V 
 Output conductor Co 22µF - 400V Figure 19. Identified working point in case 1 
 Coil L 250mH - 8A 
 Electric lock IRF250 - 200V, 30A 
 Diode SR5200 - 200V, 5A 
 Controlling circuit 
 Microprocessor Arduino Uno 
 Opto to excite MOSFET TLP250 
 Opto amp LM324 
 IC source 7809 (9V), 7815 (15V) 
 Figure 20. Identified working point in case 2 
 4. CONCLUSION 
 The paper has proposed a method of identifying and 
 solving the partial shading problem in a solar PV panel 
 configuration, in order to test a scheme to absorb the 
 maximum solar irradiance to a solar PV panel to use in DC 
 applications. 
 Figure 18. Prototype circuit The paper has also proposed a method for determining 
 the GPPs of a series of solar PV panels under partial shading 
 3.4. Experiment results 
 conditions. The results of applying the proposed method 
 The properties of the proposed algorithm are tested on a which are presented through simulation and experiment 
 solar PV cell simulator consisting of 5 solar PV panels have indicated the high feasibility for practical applications. 
 connecting in series. Due to the limitation in the 
 construction capacity, the experimental model in the paper 
 is only able to meet 400W capacity. Therefore, each panel in 
 the series is installed at the capacity of 58W. The tested loads REFERENCES 
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