An Artificial Neural Network for Solar Energy Prediction and Control Using Jaya-SMC
Abstract
:1. Introduction
- Implementation of artificial neural networks (ANN) to predict temperature and solar radiation as it is one of the most effective and efficient methods in all fields.
- Implementation of JAYA-SMC based approach to control DC-DC converters according to the maximum power point tracking concept (MPPT).
2. Methodology
2.1. PV Panel Modeling
2.2. Proposed Artificial Neuro Networks Predictive Modeling
- Step1: Data assembly, pre-processing, data conversion, and normalization. The data set used to predict the temperature and solar radiation reflected on the PV under study was obtained from the Department of Systems Engineering and Automation at the Vitoria School of Engineering of the University of the Basque Country. The data was collected using the irradiance and temperature sensor Si-V-010-T [41].
- Step2: Statistical analysis.
- Step3: Neural Network objects design.
- Step4: Network training; the algorithm of Levenberg Marquardt has been used for the training of the network. This choice has been justified by the fact that this algorithm typically requires more memory but less time. The training automatically stops when the generalization stops improving, as indicated by an increase in the mean square error of the validation samples. The Mean Squared Error is the average squared difference between outputs and targets. Lower values are better, as zero means no error. This algorithm is also improving the regression, R, and it is the value measuring the correlation between outputs and targets. A unit, R, value indicates a close relationship, while 0 denotes a random relationship.
- Step5: Simulation of network response to new entries.
- Step6: Approval and testing.
2.3. JAYA-SMC Hybrid MPPT Control of the SEPIC Chopper
2.3.1. Integrated SEPIC Chopper
2.3.2. JAYA-SMC Hybrid MPPT Control
- Jaya Method
Algorithm 1: JAYA Algorithm |
Step 1: Set the population and the maximum number of iterations NPop and Nmax. Step 2: Determine the Xbest andXworst solutions. Step 3: While gen <= ≤ Nmax For I = 1 to Npop carry out: ${X}_{n}(i,j)=X(i,j,k)+{r}_{1,i,j}({X}_{best}(j)-\left|X(i,j)\right|)-{r}_{2,i,j}({X}_{worst}(j)-\left|X(i,j)\right|)$ Obtain the update community and evaluate the new value, if the new value is more suitable than the previous one, it will replace the old one. End for; End while. Step 4: Show existing solutions X(i) and f(X(i)). |
- 2.
- Sliding Mode Control Technique
- First, design a sliding surface in state space.
- Have a selection of a control law to force the state trajectory of the system to move towards a predetermined surface in finite time.
- Maintain around this surface with appropriate switching logic.
3. Simulation Results and Discussion
4. Conclusions and Future Works
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviation List
ANN | Artificial Neural Network |
AI | Artificial intelligence |
NN | Neural networks |
R | Regression |
MSE | Mean squared error |
MPP | Maximum power point |
MPPT | Maximum power point tracking |
P&O | Perturb and observe |
Tanh | Hyperbolictangent |
OCV | Open-circuit voltage |
SCC | Short-circuit current |
PVS | Photovoltaic system |
SEPIC | Single ended primary inductor converter |
SMC | Sliding mode control |
mFFO | Modified fire-fly optimizer |
FE-SVR | Feature engineering-support vector regression |
FLC | Fuzzy logic control |
GA | Genetic algorithm |
PSO | Particle swarm optimization |
CPSO | Chaotic particle swarm optimization |
GWO | Grey wolf optimization |
PI | Proportional integral |
DC | Direct current |
PID | Proportional integral derivative |
PN | Positive negative |
IC | Incremental conductance |
SES | Solar energy system |
QSVM | Quadratic support vector machine |
CNN-BiLSTM | Convolution neural network-bi-direction long short term memory |
ANFIS | Adaptive neuron fuzzy inference system |
GMDH | Group method of data handling |
ANFIS-PSO | Adaptive neuron fuzzy inference system-particle swarm optimization |
Appendix A
Peak Power (Pmax) | 340 W |
---|---|
Voltage at Pmax (Vmp) | 36.7 V |
Current at Pmax (Imp) | 9.28 A |
Open circuit voltage (Voc) | 45.2 V |
Short circuit current (Isc) | 9.9 A |
Appendix B
Frequency PWM | 55 (KHz) | ${\mathit{L}}_{1}$ | 1.8 (mH) | ${\mathit{L}}_{2}$ | 1.4 (mH) |
---|---|---|---|---|---|
${C}_{1}$ | 120 (μF) | ${C}_{2}$ | 470 (μF) |
Appendix C
Armature Resistance Ra | 2.2 Ω |
---|---|
Armature inductance La | 5 × 10^{−3} H |
Back-emf constant | 0.015 V/rmp |
Total inertia J | 0.03 kg.m^{2} |
Viscous friction coefficient | 0.12 N.m.s |
Coulomb friction torque Tf | 0.11 N.m |
Initial speed | 3 rad/s |
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Inputs | Real Temperature | Forecasted Temperature | Error |
---|---|---|---|
(54.43;54.50;54.54) | 54.59 | 54.58 | 0.01 |
(26.15;26.15;26.06) | 25.96 | 26.00 | 0.04 |
(59.86;59.76;59.91) | 60.08 | 60.09 | 0.01 |
Input | Real Irradiation | Forecasted Irradiation | Error |
---|---|---|---|
(959.28;960.38;961.85) | 962.58 | 962.71 | 0.13 |
(548.03;547.66;546.75) | 545.65 | 546.1 | 0.45 |
(877.25;877.80;877.80) | 876.70 | 877.38 | 0.68 |
Ref. | Studied System | Model | Main Objective | Degree of Complexity | MSE | R^{2} |
---|---|---|---|---|---|---|
[51] | PVS | QSVM | Short-term energy forecasting for building integrated PV system | High | 0.16 | 0.88 |
PVS | Decision Tree | High | 0.087 | 0.88 | ||
[52] | Solar radiation-based power plants | CNN-BiLSTM | Midterm solar radiation prediction | High | 0.17 | 0.94 |
[53] | Meteorological ground stations | DL | Estimation of daily solar radiation | High | 0.6 | 0.98 |
[54] | Meteorologicalstation | ANFIS | Predict solar radiation | Low | 1.16 | 0.85 |
[55] | SES | GMDH | Estimation of daily global solar radiation | Medium | 0.05 | 0.98 |
[56] | SES | ANFIS-PSO | Monthly solar radiation prediction | High | 0.09 | 0.99 |
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Jlidi, M.; Hamidi, F.; Barambones, O.; Abbassi, R.; Jerbi, H.; Aoun, M.; Karami-Mollaee, A. An Artificial Neural Network for Solar Energy Prediction and Control Using Jaya-SMC. Electronics 2023, 12, 592. https://doi.org/10.3390/electronics12030592
Jlidi M, Hamidi F, Barambones O, Abbassi R, Jerbi H, Aoun M, Karami-Mollaee A. An Artificial Neural Network for Solar Energy Prediction and Control Using Jaya-SMC. Electronics. 2023; 12(3):592. https://doi.org/10.3390/electronics12030592
Chicago/Turabian StyleJlidi, Mokhtar, Faiçal Hamidi, Oscar Barambones, Rabeh Abbassi, Houssem Jerbi, Mohamed Aoun, and Ali Karami-Mollaee. 2023. "An Artificial Neural Network for Solar Energy Prediction and Control Using Jaya-SMC" Electronics 12, no. 3: 592. https://doi.org/10.3390/electronics12030592