Adaptive Neural Network-Based Voltage Regulation for a High-Gain Boost Converter in Solar Photovoltaic Systems
DOI:
https://doi.org/10.18196/jrc.v6i2.25606Keywords:
Adaptive Voltage Control, Artificial Neural Network (ANN), High-Gain Boost Converter, DC Bus Voltage Regulation, Solar Photovoltaic SystemsAbstract
This study proposes an adaptive Artificial Neural Network-based voltage control strategy for maintaining a stable DC bus voltage in a high-gain DC-DC boost converter for solar photovoltaic systems. Unlike conventional PID controllers, which struggle with non-linear and dynamic conditions, the proposed controller dynamically adjusts the duty cycle to mitigate the effects of varying solar irradiance and reference voltage, ensuring robust voltage regulation with reduced overshoot, enhanced transient response, and improved steady-state stability. This approach addresses critical challenges in standalone solar applications, such as water pumping and rural electrification, where consistent performance is essential despite fluctuating environmental conditions. In comparison to conventional control strategies, the ANN-based controller demonstrates superior adaptability, particularly under rapidly changing operating conditions. The results demonstrate the superior adaptability and efficiency of the ANNbased controller compared to the conventional PID controller, making it a valuable and reliable solution for sustainable solar PV systems. The proposed system was validated using a cosimulation framework that integrates MATLAB/Simulink and OrCAD, facilitating performance evaluation under varying solar irradiance and reference voltage conditions.
References
A. I. Osman et al., “Cost, environmental impact, and resilience of renewable energy under a changing climate: a review,” Environmental Chemistry Letters, vol. 21, pp. 741–764, 2023, doi: 10.1007/s10311-022- 01532-8.
S. Algarni et al., “Contribution of renewable energy sources to the environmental impacts and economic benefits for sustainable development,” Sustainable Energy Technologies and Assessments, vol. 56, 2023, doi: 10.1016/j.seta.2023.103098.
A. Raihan et al., “The dynamic impacts of economic growth, financial globalization, fossil fuel, renewable energy, and urbanization on load capacity factor in Mexico,” Sustainability, vol. 15, no. 18, 2023, doi: 10.3390/su151813462.
F. Al Mubarak, R. Rezaee, and D. A. Wood, “Economic, societal, and environmental impacts of available energy sources: a review,” Engineering, vol. 5, pp. 1232–1265, 2024, doi: 10.3390/eng5030067.
A. Zisos, D. Chatzopoulos, and A. Efstratiadis, “The Concept of Spatial Reliability Across Renewable Energy Systems—An Application to Decentralized Solar PV Energy,” Energies, vol. 17, 2024, doi: 10.3390/en17235900.
Md H. Kabir, S. M. S. Newaz, T. Kabir, and A. S. Howlader, “Integrating Solar Power with Existing Grids: Strategies, Technologies, and Challenges & Review,” International Journal of Science and Engineering, vol. 1, no. 2, pp. 48–62, 2024, doi: 10.62304/ijse.v1i2.142.
E. Bullich-Massague´ et al., “A review of energy storage technologies for large scale photovoltaic power plants,” Applied Energy, vol. 274, 2020, doi: 10.1016/j.apenergy.2020.115213.
P. Nikolaidis, “Solar Energy Harnessing Technologies towards DeCarbonization: A Systematic Review of Processes and Systems,” Energies, vol. 16, 2023, doi: 10.3390/en16176153.
N. Mlilo, J. Brown, and T. Ahfock, “Impact of intermittent renewable energy generation penetration on the power system networks – A review,” Technology and Economics of Smart Grids and Sustainable Energy, vol. 6, 2021, doi: 10.1007/s40866-021-00123-w.
O. A. Al-Shahri et al., “Solar photovoltaic energy optimization methods, challenges and issues: A comprehensive review,” Journal of Cleaner Production, vol. 284, 2021, doi: 10.1016/j.jclepro.2020.125465.
M. S. Hossain, N. A. Madlool, A. W. Al-Fatlawi, and M. El Haj Assad, “High Penetration of Solar Photovoltaic Structure on the Grid System Disruption: An Overview of Technology Advancement,” Sustainability, vol. 15, 2023, doi: 10.3390/su15021174.
T. S. Babu, K. R. Vasudevan, V. K. Ramachandaramurthy, S. B. Sani, S. Chemud, and R. M. Lajim, “A Comprehensive Review of Hybrid Energy Storage Systems: Converter Topologies, Control Strategies and Future Prospects,” IEEE Access, vol. 8, pp. 148702–148721, 2020, doi: 10.1109/ACCESS.2020.3015919.
Y. Zhou et al., “Passive and active phase change materials integrated building energy systems with advanced machine-learning based climateadaptive designs, intelligent operations, uncertainty-based analysis and optimisations: A state-of-the-art review,” Renewable and Sustainable Energy Reviews, vol. 130, 2020, doi: 10.1016/j.rser.2020.109889.
T. Sutikno, H. S. Purnama, N. S. Widodo, S. Padmanaban, and M. R. Sahid, “A review on non-isolated low-power DC–DC converter topologies with high output gain for solar photovoltaic system applications,” Clean Energy, vol. 6, no. 4, pp. 557–572, 2022, doi: 10.1093/ce/zkac037.
F. Mumtaz et al., “Review on non-isolated DC-DC converters and their control techniques for renewable energy applications,” Ain Shams Engineering Journal, vol. 12, no. 4, pp. 3747–3763, 2021, doi: 10.1016/j.asej.2021.03.022.
M. Hawsawi, H. M. D. Habbi, E. Alhawsawi, M. Yahya, and M. A. Zohdy, “Conventional and Switched Capacitor Boost Converters for Solar PV Integration: Dynamic MPPT Enhancement and Performance Evaluation,” Designs, vol. 7, no. 5, 2023, doi: 10.3390/designs7050114.
A. F. de Souza, F. L. Tofoli, and E. R. Ribeiro, “Switched Capacitor DCDC Converters: A Survey on the Main Topologies, Design Characteristics, and Applications,” Energies, vol. 14, 2021, doi: 10.3390/en14082231.
P. S. Divya, N. Sathik, J. A. L., K. V., and D. Almakhles, “A comprehensive study on various DC–DC converter voltage-boosting topologies and their applications,” Circuit World, vol. 48, no. 4, pp. 529–549, 2022, doi: 10.1108/CW-12-2020-0338.
F. Alhaj Omar, “Comprehensive Analysis and Evaluation of DC-DC Converters: Advancements, Applications, and Challenges,” Black Sea Journal of Engineering and Science, vol. 6, no. 4, pp. 557–571, 2023, doi: 10.34248/bsengineering.1357849.
D. Nayanasiri and Y. Li, “Step-Down DC–DC Converters: An Overview and Outlook,” Electronics, vol. 11, 2022, doi: 10.3390/electronics11111693.
K. Zaoskoufis and E. C. Tatakis, “An Improved Boost-Based DC/DC Converter With High-Voltage Step-Up Ratio for DC Microgrids,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 2, pp. 1837–1853, 2021, doi: 10.1109/JESTPE.2020.2981018.
T. Kobaku, R. Jeyasenthil, S. Sahoo, R. Ramchand, and T. Dragicevic, “Quantitative Feedback Design-Based Robust PID Control of Voltage Mode Controlled DC-DC Boost Converter,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 1, pp. 286–290, 2021, doi: 10.1109/TCSII.2020.2988319.
F. Yi and F. Wang, “Review of Voltage-Bucking/Boosting Techniques, Topologies, and Applications,” Energies, vol. 16, 2023, doi: 10.3390/en16020842.
M. Haris, M. Asim, and M. Tariq, “A Review of Non-Isolated High Gain DC-to-DC Converter Topologies,” in 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), pp. 1–6, 2022, doi: 10.1109/ICEFEET51821.2022.9847767.
B. T. Rao and D. De, “A Coupled Inductor-Based High-Gain ZVS DC–DC Converter With Reduced Voltage Stresses,” IEEE Transactions on Power Electronics, vol. 38, no. 12, pp. 15956–15967, 2023, doi: 10.1109/TPEL.2023.3310577.
M. Kumar, V. K. Yadav, and A. K. Verma, “Switched Capacitor Based High Gain Boost Converter for Renewable Energy Application,” IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 4, no. 3, pp. 818–826, 2023, doi: 10.1109/JESTIE.2023.3262451.
M. Rezaie and V. Abbasi, “Effective combination of quadratic boost converter with voltage multiplier cell to increase voltage gain,” IET Power Electronics, vol. 13, no. 11, pp. 2322–2333, 2020, doi: 10.1049/ietpel.2019.1070.
X. Fan, H. Sun, Z. Yuan, Z. Li, R. Shi, and N. Ghadimi, “High Voltage Gain DC/DC Converter Using Coupled Inductor and VM Techniques,” IEEE Access, vol. 8, pp. 131975–131987, 2020, doi: 10.1109/ACCESS.2020.3002902.
Y. Guan, C. Cecati, J. M. Alonso, and Z. Zhang, “Review of HighFrequency High-Voltage-Conversion-Ratio DC–DC Converters,” IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 2, no. 4, pp. 374–389, 2021, doi: 10.1109/JESTIE.2021.3051554.
M. Q. Ademola and M. A. Idowu, “The Advent of the Proportional Integral Derivative Controller: A Review,” Technology, vol. 2, 2023, doi: 10.24412/2181-1431-2023-2-5-22.
R. P. Borase, D. K. Maghade, S. Y. Sondkar, et al., “A review of PID control, tuning methods and applications,” International Journal of Dynamics and Control, vol. 9, pp. 818–827, 2021, doi: 10.1007/s40435- 020-00665-4.
G. Nethaji and J. Kathirvelan, “Performance comparison between PID and Fuzzy Logic Controllers for the hardware implementation of traditional high voltage DC-DC boost converter,” Heliyon, vol. 10, no. 17, 2024, doi: 10.1016/j.heliyon.2024.e36750.
G. C. Konstantopoulos and P. R. Baldivieso-Monasterios, “State-limiting PID controller for a class of nonlinear systems with constant uncertainties,” International Journal of Robust and Nonlinear Control, vol. 30, no. 5, pp. 1770–1787, 2020, doi: 10.1002/rnc.4853.
M. M. Sati, D. Kumar, A. Singh, M. Raparthi, F. Y. Alghayadh, and M. Soni, “Two-Area Power System with Automatic Generation Control Utilizing PID Control, FOPID, Particle Swarm Optimization, and Genetic Algorithms,” in 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), pp. 1–6, 2024, doi: 10.1109/ICAECT60202.2024.10469671.
I. Alhamrouni, N. H. Abdul Kahar, M. Salem, M. Swadi, Y. Zahroui, D. J. Kadhim, F. A. Mohamed, and M. Alhuyi Nazari, “A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions,” Applied Sciences, vol. 14, 2024, doi: 10.3390/app14146214.
M. Harib, H. Chaoui, and S. Miah, “Evolution of adaptive learning for nonlinear dynamic systems: a systematic survey,” Intelligent Robotics, vol. 2, pp. 37–71, 2022, doi: 10.20517/ir.2021.19.
A. Hashemi, G. Orzechowski, A. Mikkola, et al., “Multibody dynamics and control using machine learning,” Multibody System Dynamics, vol. 58, pp. 397–431, 2023, doi: 10.1007/s11044-023-09884-x.
S. Zhao, F. Blaabjerg, and H. Wang, “An Overview of Artificial Intelligence Applications for Power Electronics,” IEEE Transactions on Power Electronics, vol. 36, no. 4, pp. 4633–4658, 2021, doi: 10.1109/TPEL.2020.3024914.
T. B. Lopez-Garcia, A. Coronado-Mendoza, and J. A. Dom´ınguezNavarro, “Artificial neural networks in microgrids: A review,” Engineering Applications of Artificial Intelligence, vol. 95, 2020, doi: 10.1016/j.engappai.2020.103894.
A. Ramadevi et al., “Optimal Design and Performance Investigation of Artificial Neural Network Controller for Solar-and Battery-Connected Unified Power Quality Conditioner,” International Journal of Energy Research, vol. 2023, no. 1, 2023, doi: 10.1155/2023/3355124.
J. Krzywanski, M. Sosnowski, K. Grabowska, A. Zylka, L. Lasek, and A. Kijo-Kleczkowska, “Advanced Computational Methods for Modeling, Prediction and Optimization—A Review,” Materials, vol. 17, 2024, doi: 10.3390/ma17143521.
A. G. Olabi et al., “Artificial neural networks applications in partially shaded PV systems,” Thermal Science and Engineering Progress, vol. 37, 2023, doi: 10.1016/j.tsep.2022.101612.
C. B. Ali, A. H. Khan, K. Pervez, T. M. Awan, A. Noorwali, and S. A. Shah, “High Efficiency High Gain DC-DC Boost Converter Using PID Controller for Photovoltaic Applications,” in 2021 International Congress of Advanced Technology and Engineering (ICOTEN), pp. 1–7, 2021, doi: 10.1109/ICOTEN52080.2021.9493476.
M. M. S. Sulong et al., “Design and Simulation of High Gain DC-DC Boost Converter System for PV Application,” in 2021 4th International Iraqi Conference on Engineering Technology and Their Applications (IICETA), pp. 179–183, 2021, doi: 10.1109/IICETA51758.2021.9717915.
J. Ahmad, M. Zaid, A. Sarwar, C.-H. Lin, M. Asim, R. K. Yadav, M. Tariq, K. Satpathi, and B. Alamri, “A New High-Gain DC-DC Converter with Continuous Input Current for DC Microgrid Applications,” Energies, vol. 14, 2021, doi: 10.3390/en14092629.
W. Hassan et al., “Design, analysis and experimental verification of a high voltage gain and high-efficiency DC–DC converter for photovoltaic applications,” IET Renewable Power Generation, vol. 14, no. 10, pp. 1699–1709, 2020, doi: 10.1049/iet-rpg.2019.1020.
R. W. Erickson and D. Maksimovic, “The Discontinuous Conduction ´ Mode,” in Fundamentals of Power Electronics, 2020, doi: 10.1007/978- 3-030-43881-4_5.
A. W. Khawaja et al., “Angle stability improvement using optimised proportional integral derivative with filter controller,” Heliyon, vol. 10, no. 19, 2024, doi: 10.1016/j.heliyon.2024.e38944.
A. Daraz, S. A. Malik, A. T. Azar, S. Aslam, T. Alkhalifah, and F. Alturise, “Optimized Fractional Order Integral-Tilt Derivative Controller for Frequency Regulation of Interconnected Diverse Renewable Energy Resources,” IEEE Access, vol. 10, pp. 43514–43527, 2022, doi: 10.1109/ACCESS.2022.3167811.
M. G. M. Abdolrasol, S. M. S. Hussain, T. S. Ustun, M. R. Sarker, M. A. Hannan, R. Mohamed, J. A. Ali, S. Mekhilef, and A. Milad, “Artificial Neural Networks Based Optimization Techniques: A Review,” Electronics, vol. 10, 2021, doi: 10.3390/electronics10212689.
Y.-J. Liu, W. Zhao, L. Liu, D. Li, S. Tong, and C. L. P. Chen, “Adaptive Neural Network Control for a Class of Nonlinear Systems With Function Constraints on States,” IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 6, pp. 2732–2741, 2023, doi: 10.1109/TNNLS.2021.3107600.
E. Viera-Martin et al., “Artificial neural networks: a practical review of applications involving fractional calculus,” European Physical Journal Special Topics, vol. 231, pp. 2059–2095, 2022, doi: 10.1140/epjs/s11734- 022-00455-3.
T. K. Gupta and K. Raza, “Optimizing Deep Feedforward Neural Network Architecture: A Tabu Search Based Approach,” Neural Processing Letters, vol. 51, pp. 2855–2870, 2020, doi: 10.1007/s11063-020-10234-7.
N. B. Shaik, S. R. Pedapati, S. A. A. Taqvi, A. R. Othman, and F. A. A. Dzubir, “A Feed-Forward Back Propagation Neural Network Approach to Predict the Life Condition of Crude Oil Pipeline,” Processes, vol. 8, 2020, doi: 10.3390/pr8060661.
M. M. Hammad, “Deep Learning Activation Functions: Fixed-Shape, Parametric, Adaptive, Stochastic, Miscellaneous, Non-Standard, Ensemble,” arXiv preprint, 2024, doi: 10.48550/arXiv.2407.11090.
S. Y. Heng, W. M. Ridwan, P. Kumar, et al., “Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction,” Scientific Reports, vol. 12, 2022, doi: 10.1038/s41598-022-13532-3.
M. Dampfhoffer, T. Mesquida, A. Valentian, and L. Anghel, “Backpropagation-Based Learning Techniques for Deep Spiking Neural Networks: A Survey,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 9, pp. 11906–11921, 2024, doi: 10.1109/TNNLS.2023.3263008.
T. Li, G. Kou, and Y. Peng, “Improving malicious URLs detection via feature engineering: Linear and nonlinear space transformation methods,” Information Systems, vol. 91, 2020, doi: 10.1016/j.is.2020.101494.
L. Li and M. Thornton, Digital System Verification: A Combined Formal Methods and Simulation Framework, Springer Nature, 2022, doi: 10.1007/978-3-031-79815-3.
J. N. Chukwunweike et al., “Integrating deep learning, MATLAB, and advanced CAD for predictive root cause analysis in PLC systems: A multi-tool approach to enhancing industrial automation and reliability,” World Journal of Advance Research and Review, vol. 23, no. 2, pp. 1778–1790, 2024, doi: 10.30574/wjarr.2024.23.2.2631.
N. F. Ibrahim, M. M. Mahmoud, H. Alnami, D. E. M. Wapet, S. A. Ardjoun, M. I. Mosaad, A. M. Hassan, and H. Abdelfattah, “A new adaptive MPPT technique using an improved INC algorithm supported by fuzzy self-tuning controller for a grid-linked photovoltaic system,” PLoS One, vol. 18, no. 11, p. e0293613, 2023, doi: 10.1371/journal.pone.0293613.
W. Hanini and M. Ayadi, “PSpice and Simulink co-simulation for diode and AC-DC converter using SLPS interface software,” 2017 18th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), pp. 408-413, 2017, doi: 10.1109/STA.2017.8314910.
Y. Wang, Y. Zhang, H. Yang, and J. Rodriguez, “Variable-vectorbased model predictive control with reduced current harmonic and controllable switching frequency for PMSM drives,” IEEE Transactions on Power Electronics, vol. 39, no. 12, pp. 16429–16441, 2024, doi: 10.1109/TPEL.2024.3441726.
Y. Gao, S. Wang, T. Dragicevic, P. Wheeler, and P. Zanchetta, “Artificial intelligence techniques for enhancing the performance of controllers in power converter-based systems—An overview,” IEEE Open Journal of Industry Applications, vol. 4, pp. 366–375, 2023, doi: 10.1109/OJIA.2023.3338534.
D. Chen, Z. Fang, and S. Li, “A novel BSO algorithm for threelayer neural network optimization applied to UAV edge control,” Neural Processing Letters, vol. 55, pp. 6733–6752, 2023, doi: 10.1007/s11063- 023-11157-9.
L. S. Pusztahazi, G. Eigner, and O. Csisz ´ ar, “Parametric activation ´ functions for neural networks: A tutorial survey,” IEEE Access, vol. 12, pp. 168626–168644, 2024, doi: 10.1109/ACCESS.2024.3474574.
B. Stojanovic, S. Gajevi ´ c, N. Kosti ´ c, S. Miladinovi ´ c, and A. Vencl, “Op- ´ timization of parameters that affect wear of A356/Al2O3 nanocomposites using RSM, ANN, GA and PSO methods,” Industrial Lubrication and Tribology, vol. 74, no. 3, pp. 350–359, 2022, doi: 10.1108/ILT-07-2021- 0262.
A. A. Asiri, A. Shaf, T. Ali, M. Aamir, M. Irfan, and S. Alqahtani, “Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability,” PeerJ Computer Science, vol. 10, 2024, doi: 10.7717/peerj-cs.1878.
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