A Hybrid Maximum Power Point Tracking Method for Photovoltaic Systems for Dynamic Weather Conditions
Abstract
:1. Introduction
2. PV Modeling and Characteristics
3. MPPT Algorithms
3.1. P&O Algorithm
3.2. Fuzzy Logic Controller (FLC)
3.3. Proposed Hybrid MPPT Algorithm
4. Results and Discussion
4.1. Performance of FLC Alone
4.2. Performance of P&O Alone
4.3. Performance of the Hybrid Proposed Algorithm
4.4. Testing the Performance at Random Weather Conditions
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Electrical Characteristic | STP270-24/Vd |
---|---|
Optimum Operating Voltage (Vmp) | 35.0 V |
Optimum Operating Current (Imp) | 7.71 A |
Open-Circuit Voltage (Voc) | 44.5 V |
Short-Circuit Current (Isc) | 8.20 A |
Maximum Power at STC (Pmax) | 270 W |
Temperature Coefficient of Voc | −0.34%/°C |
Temperature Coefficient of Isc | 0.045%/°C |
Fuzzy Logic Type | Sugeno |
---|---|
Number of inputs | 2 |
Number of membership function | 10 |
No of TRAINING a epochs | 3000 |
Input membership function type | Gaussian |
output membership function type | Linear |
Algorithm used | Grid partitioning |
Optimization method | Hybrid |
Case No. | Weather Condition | Ir1 (Watt/m2) | Ir2 (Watt/m2) | Nominal Power (Watt) | Power after Fuzzy (Watt) | Efficiency |
---|---|---|---|---|---|---|
1 | Uniform Irradiation | 1000 | 1000 | 258 | 258 | 100% |
2 | 900 | 900 | 232 | 232 | 100% | |
3 | 800 | 800 | 207 | 206 | 99.6% | |
4 | 700 | 700 | 181 | 179.1 | 99% | |
5 | 600 | 600 | 155 | 154.2 | 99.5% | |
6 | 500 | 500 | 128.5 | 127.6 | 99.3% | |
7 | 400 | 400 | 103.2 | 102.6 | 99.6% | |
8 | 740 | 740 | 190.3 | 163.4 | 85.7 | |
9 | 585 | 585 | 150.63 | 139.2 | 92.4 | |
10 | 597 | 597 | 153.8 | 151.6 | 98.5 | |
11 | Partial Shading | 1000 | 300 | 128.1 | 122.5 | 96% |
12 | 800 | 300 | 101.4 | 99.3 | 98% | |
13 | 500 | 200 | 58.1 | 56.2 | 96.7% | |
14 | 400 | 100 | 46.3 | 44.8 | 96.5% | |
15 | 700 | 300 | 48.7 | 47.5 | 99% | |
16 | 892 | 407 | 126.3 | 106.8 | 84.4% | |
17 | 644 | 596 | 103.7 | 92.3 | 89.0% | |
18 | 400 | 100 | 46.3 | 38 | 82.1% |
Controller | Accuracy | Convergence | Oscillations | Trapping |
---|---|---|---|---|
Fuzzy | Moderate | Fast | Low | No |
P&O/large step size | Low | Fast | High | Yes |
P&O/small step size | High | Slow | Moderate | Yes |
Hybrid | High | Fast | Moderate | No |
Case No. | Ir1 (Watt/m2) | Ir2 (Watt/m2) | Nominal Power (Watt) | FLC Alone (Watt) | Hybrid Proposed Algorithm (Watt) |
---|---|---|---|---|---|
1 | 1000 | 300 | 128.1 | 122.5 | 127.9 |
2 | 800 | 300 | 101.4 | 99.3 | 101.2 |
3 | 500 | 200 | 58.1 | 56.2 | 57.9 |
4 | 400 | 100 | 46.3 | 44.8 | 46.2 |
5 | 700 | 300 | 48.7 | 47.5 | 48.5 |
6 | 892 | 407 | 126.3 | 106.8 | 126.2 |
7 | 644 | 596 | 103.7 | 92.3 | 103.5 |
8 | 400 | 100 | 46.3 | 38 | 46.1 |
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Bataineh, K.; Eid, N. A Hybrid Maximum Power Point Tracking Method for Photovoltaic Systems for Dynamic Weather Conditions. Resources 2018, 7, 68. https://doi.org/10.3390/resources7040068
Bataineh K, Eid N. A Hybrid Maximum Power Point Tracking Method for Photovoltaic Systems for Dynamic Weather Conditions. Resources. 2018; 7(4):68. https://doi.org/10.3390/resources7040068
Chicago/Turabian StyleBataineh, Khaled, and Naser Eid. 2018. "A Hybrid Maximum Power Point Tracking Method for Photovoltaic Systems for Dynamic Weather Conditions" Resources 7, no. 4: 68. https://doi.org/10.3390/resources7040068