A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems
Abstract
:1. Introduction
2. Modelling of a Photovoltaic Cell
3. DC-DC Boost Converter
4. MPPT Control
4.1. MPPT Algorithms
4.2. Perturb and Observe Method
4.3. Incremental Conductance Method
4.4. Fuzzy Logic Controller Method
4.4.1. Fuzzification
4.4.2. Fuzzy Inference System (FIS)
4.4.3. Defuzzification
4.5. Neural Network Method
- Solar panel data used for training the neural network and ANFIS
- Short circuit current = 8.66 A;
- Maximum power point current = 8.15 A;
- Open-source voltage = 37.3 V;
- Maximum power point voltage = 30.7;
- Alpha = 0.086998;
- Beta = −0.36901;
4.6. ANFIS Method (Adaptive Neuro-Fuzzy Inference System)
Adaptive Neuro-Fuzzy Controller
- Rule i: if E(k) is Xi1 and CE(k) is Xi2, then
- (Duty)i is the changing duty cycle and
- Xij is the membership function.
4.7. Hybrid Method (Neural Network and P&O)
5. Results and Discussion
6. Conclusions
7. Future Scope
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Parameter | Specifications |
---|---|
Maximum power | 250.20 W |
Open circuit voltage | 37.3 V |
Voltage at maximum power point | 30.7 V |
Short circuit current | 8.66 A |
Current at maximum power point | 8.15 A |
Parameter | Time (sec) | Value |
---|---|---|
Irradiance | (0, 0.2, 0.4, 0.6, 0.8) | (1000, 800, 600, 400, 200) w/m2 |
Temperature Load | (0, 0.2, 0.4, 0.6, 0.8) (0, 0.3, 0.6) | (−15, 10, 25, 30, 45) Celsius (20, 30, 40) Ω |
NB | NS | ZE | PS | PB | |
---|---|---|---|---|---|
NB | PB | PS | NB | NS | NS |
NS | PS | PS | NB | NS | NS |
ZE | NS | NS | NS | PB | PB |
PS | NS | PB | PS | NB | PB |
PB | NB | NB | PB | PS | PB |
Specifications | Data | Validation and Test Data of 1000 Samples | ||
---|---|---|---|---|
Toolbox | NNTOOL box | Type of Sample | Samples (%) | Total Samples |
Wizard: Input-output and curve fitting | Fitting app | Training | 70% | 700 samples |
Input data to network | 1000 points input data of irradiation, temperature | Validation | 15% | 150 samples |
Target data/desired network output | 1000 points data of voltage | Testing | 15% | 150 samples |
Samples | Matrix—rows | |||
Number of hidden neurons | 10 | |||
Training Algorithm | Levenberg–Marquardt |
Items | P&O Method | Incremental Conductance Method | Fuzzy Logic Control Method | ANFIS Method | Neural Network Method | Hybrid Controller Model |
---|---|---|---|---|---|---|
Dynamic behaviour | Poor | Medium | Medium | Good | Good | Fast |
Transient behaviour | Bad | Bad | Good | Good | Good | Fast |
(oscillations) Steady-state | Large | Moderate | Small | Small | Small | Very small |
requirements | P&O algorithm | Incremental conductance algorithm | Fuzzy logic membership functions | ANFIS training data | Neural network training data | Neural network and P&O combined |
Static error | High | High | Low | Low | Low | low |
Controller accuracy | Low | Medium | Accurate | Accurate | Accurate | Accurate |
Tracking speed | Slow | Slow | Fast | Fast | Fast | Faster |
System complexity | Simple power calculations | Simple | Medium | Medium | Medium | Medium |
Temperature characteristics | Poor | Poor | Good | Good | Good | Better |
Parameters tuning | No | No | Yes | Yes | Yes | Yes |
MPPT Method | Convergence Time(s) | Irradiation: 1000 w/m2 | Values | Comment |
---|---|---|---|---|
P_max: 250 w/m2 | ||||
P&O method | 0.004 | P_avg | 237.4 | Oscillations occur |
%ɳPV | 94.96 | |||
Incremental Conductance method | 0.006 | P_avg | 239.1 | Oscillations occur |
%ɳPV | 95.60 | |||
Fuzzy logic control method | 0.04 | P_avg | 242.2 | Long convergence time |
%ɳPV | 96.88 | |||
Neural network method | 0.205 | P_avg | 244.6 | Better dynamic performance |
%ɳPV | 97.84 | |||
ANFIS control method | 0.046 | P_avg | 244.4 | Under dynamic response |
%ɳPV | 97.76 | |||
Hybrid Method | 0.2005 | P_avg | 247 | Fast response |
%ɳPV | 98.80 |
Method Name | Real-Time (s) (Varying Conditions) | Real-Time (s) (Constant Conditions) |
---|---|---|
P&O | 4.633 | 3.875 |
Incremental conductance | 9.2635 | 5.771 |
Fuzzy logic control | 423.88 | 372.41 |
Neural network | 7.701 | 4.6345 |
ANFIS | 50.5595 | 41.07 |
Hybrid | 6.154 | 5.667 |
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Devarakonda, A.K.; Karuppiah, N.; Selvaraj, T.; Balachandran, P.K.; Shanmugasundaram, R.; Senjyu, T. A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems. Energies 2022, 15, 8776. https://doi.org/10.3390/en15228776
Devarakonda AK, Karuppiah N, Selvaraj T, Balachandran PK, Shanmugasundaram R, Senjyu T. A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems. Energies. 2022; 15(22):8776. https://doi.org/10.3390/en15228776
Chicago/Turabian StyleDevarakonda, Ashwin Kumar, Natarajan Karuppiah, Tamilselvi Selvaraj, Praveen Kumar Balachandran, Ravivarman Shanmugasundaram, and Tomonobu Senjyu. 2022. "A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems" Energies 15, no. 22: 8776. https://doi.org/10.3390/en15228776