Published June 30, 2026 | Version v1
Journal article Open

ARTIFICIAL INTELLIGENCE-OPTIMIZED ADAPTIVE PULSE WIDTH MODULATION CONTROL FOR GRID-CONNECTED PHOTOVOLTAIC INVERTERS

Authors/Creators

Contributors

Description

ABSTRACT: The increasing penetration of grid-connected photovoltaic (PV) systems, particularly in three-phase applications, has intensified the need for inverter control strategies that simultaneously maximize energy efficiency and ensure high power quality under variable operating conditions. The problem. Conventional Pulse Width Modulation (PWM) control strategies for three-phase grid-connected PV inverters face an inherent trade-off between energy efficiency and power quality. Fixed and adaptive PWM techniques often struggle to maintain optimal performance across changing irradiance levels and grid conditions, while advanced methods frequently increase system complexity without delivering consistent gains in both efficiency and Total Harmonic Distortion (THD). Goal. The goal of this study is to conduct a comprehensive comparative analysis of widely used PWM control strategies and to develop an Artificial Intelligence–Optimized Adaptive PWM (AIO-PWM) algorithm capable of dynamically balancing efficiency and power quality in three-phase PV inverter systems. Methodology. The proposed AIO-PWM algorithm is benchmarked against four conventional methods: Multi-Objective Adaptive PWM (MOA-PWM), Fixed Frequency PWM, Hysteresis Current Control, and Predictive Current Control. AIO-PWM employs reinforcement learning–inspired techniques to dynamically optimize the switching frequency and modulation index based on real-time operating conditions. The control framework evaluates multiple candidate solutions using a weighted multi-criterion scoring system that balances efficiency and THD objectives. Online learning mechanisms track historical performance data, while exploration–exploitation strategies enable continuous adaptation to changing conditions. Performance evaluation is carried out using real solar irradiance data obtained from NASA POWER for a North African location (32.49°N, 3.67°E) in a three-phase simulation environment. Results. Simulation results show that AIO-PWM achieves an average efficiency of 92.68%, outperforming the MOA-PWM baseline (91.76%) by 0.92%, and delivering an additional 11.35 kWh of energy compared to Fixed PWM. The proposed method maintains a competitive THD of 3.02% and achieves the highest Power Quality Factor (57.77) among all evaluated algorithms. Scientific novelty. The scientific novelty of this work lies in the integration of reinforcement learning–inspired adaptive optimization within a PWM control framework, enabling continuous online tuning of control parameters through historical performance evaluation and multi-objective decision-making in three-phase PV inverter applications. Practical value. Economic analysis indicates a 1.8-year payback period for AIO-PWM compared to 0.6 years for MOA-PWM, justifying the additional investment for premium three-phase applications where both efficiency and power quality are critical. The results provide practical guidance for selecting PWM control strategies based on technical performance and economic considerations in three-phase grid-connected PV systems.

Files

12 Sara_Hiba_Algeria.pdf

Files (1.2 MB)

Name Size Download all
md5:a1f17e3248786e21d6e090f28c0f8774
1.2 MB Preview Download

Additional details

Software

Repository URL
https://www.ajme.ro
Development Status
Active

References

  • Alfred, D., Czarkowski, D. & Teng, J. (2024). Reinforcement Learning-Based Control of a Power Electronic Converter. Mathematics, 12(5): 671. available at: https://doi.org/10.3390/math12050671 Alharbi, Y., Darwish, A. & Ma, X. (2025). A Review of Model Predictive Control for Grid-Connected PV Applications. Electronics, 14(4): 667. available at: https://doi.org/10.3390/electronics14040667 Andrea, C.Q., Batista, E.A., Pereira, L.F.d.S.C., de Brito, M.A.G. & de Souza, G.V. (2024). LMI-based mpc design applied to the single-phase pwm inverter with lc filter under uncertain parameters. Sensors, 24(13): 4325. available at: https://doi.org/10.3390/s24134325 Babu, N.P. (2024). Adaptive Grid-Connected Inverter Control Schemes for Power Quality Enrichment in Microgrid Systems: Past, Present, and Future Perspectives. Electric Power Systems Research, 230: 110288. available at: https://doi.org/10.1016/j.epsr.2024.110288 Bebboukha, A., Meneceur, R., Chouaib, L., Anees, M.A., Elsanabary, A., Mekhilef, S., Bakeer, A., Harbi, I., Zaitsev, I. & Bajaj, M. (2024). Finite control set model predictive current control for three phase grid connected inverter with common mode voltage suppression. Scientific Reports, 14(1): 19832. available at: https://doi.org/10.1038/s41598-024-71051-9 Bushra, E., Zeb, K., Ahmad, I. & Khalid, M. (2024). A comprehensive review on recent trends and future prospects of PWM techniques for harmonic suppression in renewable energies-based power converters. Results in Engineering, 22: 102213. available at: https://doi.org/10.1016/j.rineng.2024.102213 Busacca, A., Di Tommaso, A.O., Miceli, R., Nevoloso, C., Schettino, G., Scaglione, G., Viola, F. & Colak, I. (2022). Switching Frequency Effects on the Efficiency and Harmonic Distortion in a Three-Phase Five-Level CHBMI Prototype with Multicarrier PWM Schemes: Experimental Analysis. Energies, 15(2): 586. available at: https://doi.org/10.3390/en15020586 Cao, T., Ye, Z., Wu, Q., Wan, X., Wang, J. & Li, D. (2025). A Review of Adaptive Control Methods for Grid-Connected PV Inverters in Complex Distribution Systems. Energies, 18(3): 473. available at: https://doi.org/10.3390/en18030473 Chakir, F., El Magri, A., Kissaoui, M., Lajouad, R., El Fadili, A. & Chakir, M. (2024). Advanced control strategies for multilevel inverter in grid-connected and off-grid photovoltaic systems: A multi-objective approach using ls-pwm for thd reduction. Scientific African, 26: e02472. available at: https://doi.org/10.1016/j.sciaf.2024.e02472 Chavali, R.V., Dey, A. & Das, B. (2024). A performance comparison of hysteresis current control techniques for D-STATCOM application. Electric Power Systems Research, 234: 110519. available at: https://doi.org/10.1016/j.epsr.2024.110519 Dubey, S., Sarvaiya, J.N. & Seshadri, B. (2013). Temperature Dependent Photovoltaic (PV) Efficiency and Its Effect on PV Production in the World – A Review. Energy Procedia, 33: 311–321. available at: https://doi.org/10.1016/j.egypro.2013.05.072 Gonzales-Zurita, Ó., Clairand, J.M., Peñalvo-López, E. & Escrivá-Escrivá, G. (2020). Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids. Energies, 13(13): 3483. available at: https://doi.org/10.3390/en13133483 Hafezi, H. & Faranda, R. (2021). A New Approach for Power Losses Evaluation of IGBT/Diode Module. Electronics, 10(3): 280. available at: https://doi.org/10.3390/electronics10030280 Hasheminasab, S., Alzayed, M. & Chaoui, H. (2024). A Review of Control Techniques for Inverter-Based Distributed Energy Resources Applications. Energies, 17(12): 2940. available at: https://doi.org/10.3390/en17122940 IEEE Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces. (2018). IEEE Std 1547-2018 (Revision of IEEE Std 1547-2003), 1-138. available at: https://doi.org/10.1109/IEEESTD.2018.8332112 Khan, A., Yasir, M., Liu, H., Yang, Z. & Yuan, X. (2020). A Comprehensive Review on Grid Connected Photovoltaic Inverters, Their Modulation Techniques, and Control Strategies. Energies, 13(16): 4185. available at: https://doi.org/10.3390/en13164185 Li, P., Li, R., He, Y. & Zhang, J. (2021). Adaptive finite control set model predictive control for three-phase inverters connected to distorted grid with fewer voltage sensors. Control Engineering Practice, 116: 104936. available at: https://doi.org/10.1016/j.conengprac.2021.104936 Martín-Arroyo, S., Cañete, D., Ciudad, J.H. & García-Gracia, M. (2024). Core Losses Analysis of the LCL Filter Inductor for SiC-Based Inverter. Renewable Energy and Power Quality Journal, 20(3): 281–22. available at: https://doi.org/10.24084/repqj20.281 NASA Langley Research Center (LaRC) POWER Project. (2023). NASA POWER Data Access Viewer. available at: https://power.larc.nasa.gov/ Accessed: 2024-01-15. Santos, L.d.O., de Carvalho, P.C.M. & Filho, C.d.O.C. (2022). Photovoltaic Cell Operating Temperature Models: A Review of Correlations and Parameters. IEEE Journal of Photovoltaics, 12(1): 179–190. available at: https://doi.org/10.1109/JPHOTOV.2021.3113156 Skoplaki, E. & Palyvos, J.A. (2009). Operating temperature of photovoltaic modules: A survey of pertinent correlations. Renewable Energy, 34(1): 23–29. available at: https://doi.org/10.1016/j.renene.2008.04.009 Stöttner, J., Rauscher, A. & Endisch, C. (2022). Pareto optimization of multilevel inverter structures regarding the DC magnitude, switching frequency and switching angles. International Journal of Electrical Power & Energy Systems, 142: 108259. available at: https://doi.org/10.1016/j.ijepes.2022.108259 Trivedi, T.A., Jadeja, R. & Bhatt, P. (2015). A Review on Direct Power Control for Applications to Grid Connected PWM Converters. Engineering, Technology & Applied Science Research, 5(4): 841–849. available at: https://doi.org/10.48084/etasr.544 Wang, F., Song, Y., Dou, W., Zheng, Z., Li, Z., Li, B. & Liu, J. (2025). High Power Density IGBT Loss Calculation Model and Analysis. Energies, 18(4): 997. available at: https://doi.org/10.3390/en18040997 Wang, F., Tuluhong, A., Luo, B. & Abudureyimu, A. (2025). Control Methods and AI Application for Grid-Connected PV Inverter: A Review. Technologies, 13(11): 535. available at: https://doi.org/10.3390/technologies13110535 Zaid, S.A., Bakeer, A., Albalawi, H., Alatwi, A.M., Abdeldaim, H. & Manqarah, B. (2023). Model-Free Predictive Current Control of a 3-ϕ Grid-Connected Neutral-Point-Clamped Transformerless Inverter. Energies, 16(7): 3141. available at: https://doi.org/10.3390/en16073141 Zhou, L., Hou, L., Wang, H. & Liu, Y. (2024). Research on Multi Vector Model Predictive Power Control of Grid Connected Inverter under Low Voltage Ride Through. Journal of Electrical Engineering, 19(2): 83–91. available at: https://doi.org/10.11985/2024.02.010