Optimization-Based PID Controller Design for DC-DC Boost Converters in Hybrid Renewable Energy Systems

Year 2025, Volume: 8 Issue: 2, 455 - 461, 15.03.2025

Mustafa Güneş , Funda Demir

https://doi.org/10.34248/bsengineering.1613222

Abstract

The increasing awareness of the need for renewable and clean energy sources has become a significant agenda item, especially as global energy demand continues to rise. Studies on renewable energy systems, which provide healthier conditions for current and future generations while meeting energy demand, are becoming increasingly widespread both locally and globally. Hybrid energy systems, formed by combining multiple energy sources, have recently introduced innovative solutions for the integration and management of various energy types. However, the voltage levels obtained from these systems are often low, making it necessary to boost the voltage for storage and household use.To address this, DC-DC boost converters are used to increase the voltage generated by solar panels, wind turbines, or hybrid energy systems. PID (Proportional-Integral-Derivative) controllers are typically required for converter control. However, conventional constant-gain PID controllers and classical PID tuning methods are often ineffective, as they rely on mathematical formulations or experimental system response analyses. To overcome this challenge, meta-heuristic optimization algorithms provide a viable alternative, offering a more stable and faster system response. In this study, a hybrid energy system consisting of a Proton Exchange Membrane (PEM) fuel cell, PV panel, and wind turbine was modeled in the Matlab/Simulink environment. A DC-DC boost converter was designed to elevate the system's output voltage to the desired reference level, enhancing system stability. Three different optimization methods—Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Artificial Bee Colony (ABC) algorithms—were employed to adjust the parameters of the PID controller used for converter control. The PID coefficients obtained through these optimization algorithms are presented and compared. The performance of the tuned PID controller was evaluated through system response analysis under variable load conditions and by calculating the Root Mean Square Error (RMSE) between the output voltage and the specified reference value. Additionally, the controller performance was analyzed based on overshoot, settling time, and rise time values as shown in the resulting graphs.

Keywords

Renewable energy, Proton exchange membrane, DC-DC boost convertor, Partial swarm optimization, Grey wolf optimization, Artificial bee colony

Ethical Statement

Ethics committee approval was not required for this study because of there was no study on animals or humans.

References

• Amirsadri S, Seyed Jalaleddin M, Hossein E-K. 2018. A Levy flight-based grey wolf optimizer combined with back-propagation algorithm for neural network training. Neural Comput Appl, 30(12): 3707–3720. https://doi.org/10.1007/s00521-017-2952-5
• Daraz A, Basit A, Zhang G. 2023. Performance analysis of pid controller and fuzzy logic controller for dc-dc boost converter. Plos One, 18(10): e0281122. https://doi.org/10.1371/journal.pone.0281122
• Harish VSKV, Kumar A. 2014. Demand side management in ındia: action plan, policies and regulations. Renew Sust Ener Rev, 33: 613-624. https://doi.org/10.1016/j.rser.2014.02.021
• Hassan Q, Victor P, Al-Musawi TJ, Ali BM, Algburi S, Alzoubi HM, Khudhair AAJ, Sameen AZ, Salman HM, Jaszczur M. 2024. The renewable energy role in the global energy transformations. Renew Ener Focus, 48: 100545. https://doi.org/10.1016/j.ref.2024.100545
• Hossam F, Aljarah I, Al-Betar MA, Mirjalili S. 2018. Grey wolf optimizer: a review of recent variants and applications. Neural Comput Appl, 30(2): 413-435. https://doi.org/10.1007/s00521-017-3272-5
• Karaboga D. 2005. An idea based on honey bee swarm for numerical optimization. Erciyes University Engineering Faculty, Computer Engineering Department Technical Report, Kayseri, Türkiye, pp: 1-10.
• Kennedy J, Eberhart R. 1995. Particle swarm optimization. In Proceedings of ICNN'95-International Conference on Neural Networks, November 27, Perth, Australia, 4: 1942-1948. https://doi.org/10.1109/ICNN.1995.488968
• Krishna KS, Kumar KS. 2015. A review on hybrid renewable energy systems. Renew Sust Ener Rev, 52: 907-916. https://doi.org/10.1016/j.rser.2015.07.187
• Le L. M, Ly HB, Pham BT, Le VM, Pham TA, Nguyen DH, Tran XT, Le TT. 2019. Hybrid artificial intelligence approaches for predicting buckling damage of steel columns under axial compression. Materials, 12(10): 1670. https://doi.org/10.3390/ma12101670
• Liu X, Wu X. 2023. A two-stage bidirectional dc-dc converter system and its control strategy. Energy, 266: 126462. https://doi.org/10.1016/j.energy.2022.126462.
• Long Y, Liu X. 2024. Optimal green investment strategy for grid-connected microgrid considering the impact of renewable energy source endowment and incentive policy. Energy, 295: 131073. https://doi.org/10.1016/j.energy.2024.131073
• Ma T, Yang H, Lu L. 2013. Performance evaluation of a stand-alone photovoltaic system on an isolated island in Hong Kong. App Ener 112: 663-672. https://doi.org/10.1016/j.apenergy.2012.12.004
• Mekhilef S, Saidur R, Kamalisarvestani M. 2012. Effect of dust, humidity and air velocity on efficiency of photovoltaic cells. Renew Sust Ener Rev, 16(5): 2920-2925. https://doi.org/10.1016/j.rser.2012.02.012
• Mirjalili S, Mirjalili SM, Lewis A. 2014. Grey wolf optimizer. Adv Eng Software, 69: 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
• Mirzaei A, Awang J, Zainal S. 2012. Design and implementation of high efficiency non-isolated bidirectional zero voltage transition pulse width modulated dc-dc converters, Energy, 47(1): 358-369. https://doi.org/10.1016/j.energy.2012.09.035
• Mitra L, Swain N. 2014. Closed loop control of solar powered boost converter with pid controller. IEEE International Conference On Power Electronics, Drives And Energy Systems, Decenber 16-19, Mumbai, India, pp: 1-5. https://doi.org/10.1109/pedes.2014.7041973
• Olatomiwa l, Mekhilef S, Ismail MS, Moghavvemi M. 2016. Energy management strategies in hybrid renewable energy systems: A review. Renew Sust Ener Rev, 62: 821-835. https://doi.org/ 10.1016/j.rser.2016.05.040
• Ozdemir A, Erdem Z. 2018. Double-loop pi controller design of the dc-dc boost converter with a proposed approach for calculation of the controller parameters. J Syst Control Eng, 232(2): 137-148.
• Paliwal P, Patidar NP, Nema RK. 2014. Planning of grid integrated distributed generators: a review of technology, objectives and techniques. Renew Sust Ener Rev, 40: 557–570. https://doi.org/10.1016/j.rser.2014.07.200
• Paraschiv S. 2023. Analysis of the variability of low-carbon energy sources, nuclear technology and renewable energy sources, in meeting electricity demand. Ener Rep, 9(s11): 276–283. https://doi.org/10.1016/j.egyr.2023.09.008
• Pareek S, Kishnani M, Gupta R. 2014. Optimal tuning of pid controller using meta heuristic algorithms. International Conference on Advances in Engineering and Technology Research, August 1-2, Kanpur, India, pp: 1–5. https://doi.org/10.1109/ıcaetr.2014.7012816
• Paul AK, Ram BS, Kulkarni SV. 2024. Review of coupled inductors in power electronics: from concept to practice. E-Prime Adv Electr Eng Electron Ener, 8: 100501. https://doi.org/10.1016/j.prime.2024.100501
• Prasad T, Raj S, Kundu D. 2024. Artificial neural network framework for the selection of deep eutectic solvents promoted enhanced oil recovery by interfacial tension reduction mechanism. Energy, 307: 132602. https://doi.org/10.1016/j.energy.2024.132602
• Sakly J, Abdelghani ABB, Slama-Belkhodja I, Sammoud H. 2017. Reconfigurable dc/dc converter for efficiency and reliability optimization. IEEE J Emerg Selected Topics Power Electron, 5(3): 1216–1224. https://doi.org/10.1109/jestpe.2017.2676027
• Solaiman HM, Hasan MM, Mohammad A, Kawsar SR, Hassan MA. 2015. Performance analysis of dc to dc boost converter using different control methods. IEEE International Conference on Electrical, Computer and Communication Technologies (ICECC), March 5-7, Tamilnadu, India, pp: 1–5.
• Sonmez M, Akgüngör MA, Bektaş S. 2017. Estimating transportation energy demand in Turkey using the artificial bee colony algorithm. Energy, 122(2017): 301–310. https://doi.org/10.1016/j.energy.2017.01.074
• Wang S, Liu H, Gao K, Zhang J. 2019. A multi-species artificial bee colony algorithm and its application for crowd simulation. IEEE Access, 7: 2549–2558.
• Xiao X, Zhang X, Song M, Liu X, Huang G. 2024. Npp accident prevention: integrated neural network for coupled multivariate time series prediction based on pso and its application under uncertainty analysis for npp data. Energy, 305: 132374. https://doi.org/10.1016/j.energy.2024.132374
• Yousef AM, Ebeed M, Abo-Elyousr FK, Elnozohy A, Mohamed M, Abdelwahab SAM. 2020. Optimization of pid controller for hybrid renewable energy system using adaptive sine cosine algorithm. Int J Renew Ener Res, 10(2): 669–677. https://doi.org/10.20508/ijrer.v10i2.10685.g7938