Stability of metaheuristic PID controllers in photovoltaic dc microgrids

• Autorzy: Yusubov Elvin , Bekirova Lala

Identyfikatory

DOI

10.35784/iapgos.6410

Warianty tytułu

PL

Stabilność metaheurystycznych regulatorów PID w fotowoltaicznych mikrosieciach prądu stałego

• Języki publikacji: EN

Abstrakty

EN

This article presents the stability assessment of metaheuristic PID controllers in the hierarchical control system of photovoltaic DC microgrids. Stability is a critical aspect of DC microgrid systems. PID controllers are utilized at the primary, secondary and tertiary control levels of the DC microgrid’s hierarchical control system. Tuning of multiple PID controllers using traditional methods such as Ziegler-Nichols and Cohen-Coon tuning techniques becomes challenging under dynamic conditions of photovoltaic DC microgrids. Metaheuristic optimization algorithms are used to construct self-tuning PID controllers with improved stability. Experiments are performed to assess the stability of PID controlled system. Results exhibit that metaheuristic-tuned PID controllers of photovoltaic DC microgrids achieved superior performance in comparison with the traditional methods.

PL

W artykule przedstawiono ocenę stabilności metaheurystycznych regulatorów PID w hierarchicznym systemie sterowania fotowoltaicznych mikrosieci DC. Stabilność jest krytycznym aspektem systemów mikrosieci DC. Regulatory PID są wykorzystywane na podstawowym, drugim i trzecim poziomie hierarchicznego systemu sterowania mikrosieci DC. Dostrajanie wielu regulatorów PID przy użyciu tradycyjnych metod, takich jak techniki Zieglera-Nicholsa i Cohena-Coona, staje się wyzwaniem w dynamicznych warunkach fotowoltaicznych mikrosieci DC. Metaheurystyczne algorytmy optymalizacji są wykorzystywane do konstruowania samostrojących regulatorów PID o zwiększonej stabilności. Przeprowadzono eksperymenty w celu oceny stabilności systemu sterowanego PID. Wyniki pokazują, że dostrojone metaheurystycznie regulatory PID fotowoltaicznych mikrosieci DC osiągnęły lepszą wydajność w porównaniu z tradycyjnymi metodami.

Słowa kluczowe

EN

DC microgrid control system; whale optimization; particle swarm optimization; photovoltaic systems; metaheuristic PID controller

PL

system sterowania mikrosieciami DC; optymalizacja wieloryba; optymalizacja roju cząstek; systemy fotowoltaiczne; metaheurystyczny regulator PID

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2025
• Tom: T. 15, nr 1
• Strony: 15--21
• Opis fizyczny: Bibliogr. 34 poz., tab., wykr.

Twórcy

autor

Yusubov Elvin

• Azerbaijan State Oil and Industry University, Department of Instrumentation Engineering, Baku, Azerbaijan

• https://orcid.org/0000-0001-6199-9266

autor

Bekirova Lala

• Azerbaijan State Oil and Industry University, Department of Instrumentation Engineering, Baku, Azerbaijan

• https://orcid.org/0000-0003-0584-7916

Bibliografia

• [1] Abbas I. A., Mustafa M. K.: A review of adaptive tuning of PID-controller: Optimization techniques and applications. International Journal of Nonlinear Analysis and Applications 15(2), 2024, 29–37 [https://doi.org/10.22075/ijnaa.2023.21415.4024].
• [2] Abhishek A. et al.: Review of Hierarchical Control Strategies for DC Microgrid. IET Renewable Power Generation 14(10), 2020, 1631–1640 [https://doi.org/10.1049/iet-rpg.2019.1136].
• [3] Ahmed O. A., Bleijs J. A. M.: An Overview of DC–DC Converter Topologies for Fuel Cell-Ultracapacitor Hybrid Distribution System. Renewable and Sustainable Energy Reviews 42, 2015, 609–626 [https://doi.org/10.1016/j.rser.2014.10.067].
• [4] Belgacem M. et al.: Performance Comparison of DC-DC Converters for Photovoltaic System. 2nd International Conference on Signal, Control and Communication (SCC), Tunis, Tunisia, 2021, 214–219 [https://doi.org/10.1109/scc53769.2021.9768353].
• [5] Borase R. P. et al.: A Review of PID Control, Tuning Methods and Applications. International Journal of Dynamics and Control 9(2), 818–827 [https://doi.org/10.1007/s40435-020-00665-4].
• [6] Chauhan, R. K. et al.: Design and Analysis of PID and Fuzzy-PID Controller for Voltage Control of DC Microgrid. Innovative Smart Grid Technologies - Asia (ISGT ASIA), 2015 [https://doi.org/10.1109/isgt-asia.2015.7387019].
• [7] Czarkowski D.: DC–DC Converters. Elsevier EBooks, 2011 [https://doi.org/10.1016/b978-0-12-382036-5.00013-6].
• [8] Debnath M. K. et al.: Gravitational Search Algorithm (GSA) Optimized Fuzzy PID Controller Design for Load Frequency Control of an Interconnected Multi-Area Power System. International Conference on Circuit, Power and Computing Technologies (ICCPCT), Nagercoil, India, 2016, 1–6 [https://doi.org/10.1109/iccpct.2016.7530205].
• [9] Durga V., Kumar P. B.: PID Controller Design with Cuckoo Search Algorithm for Stable and Unstable SOPDT Processes. IOP Conference Series Materials Science and Engineering 1091(1), 2021, 012059–012059 [https://doi.org/10.1088/1757-899x/1091/1/012059].
• [10] Ebrahimi R. et al.: Application of DC-DC Converters at Renewable Energy. Nanogenerators and Self-Powered Systems, 20 Dec. 2022 [https://doi.org/10.5772/intechopen.108210].
• [11] Ellis G.: Four Types of Controllers. Control System Design Guide 2012, 97–119 [https://doi.org/10.1016/b978-0-12-385920-4.00006-0].
• [12] Gad A. G.: Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review. Archives of Computational Methods in Engineering 29(5), 2022, 2531–2561 [https://doi.org/10.1007/s11831-021-09694-4].
• [13] Gao C. et al.: A Multi-Strategy Enhanced Hybrid Ant–Whale Algorithm and Its Applications in Machine Learning. Mathematics 12(18), 2024, 2848–2848 [https://doi.org/10.3390/math12182848].
• [14] Ghasemi M. et al.: A Self-Competitive Mutation Strategy for Differential Evolution Algorithms with Applications to Proportional–Integral–Derivative Controllers and Automatic Voltage Regulator Systems. Decision Analytics Journal 7, 2023, 100205–100205 [https://doi.org/10.1016/j.dajour.2023.100205].
• [15] Ibrahim A.-W. et al.: Cuckoo Search Combined with PID Controller for Maximum Power Extraction of Partially Shaded Photovoltaic System. Energies 15(7), 2022, 2513–2513 [https://doi.org/10.3390/en15072513].
• [16] Irwanto M. et al.: Effect of temperature and solar irradiance on the performance of 50 Hz photovoltaic wireless power transfer system. Jurnal Teknologi 85(2), 2023, 53–67 [https://doi.org/10.11113/jurnalteknologi.v85.18872].
• [17] Jiang R. et al.: A Proportional, Integral and Derivative Differential Evolution Algorithm for Global Optimization. Expert Systems with Applications 206, 2022, 117669 [https://doi.org/10.1016/j.eswa.2022.117669].
• [18] Johansson B.: Improved Models for DC-DC Converters. [Licentiate Thesis, Industrial Electrical Engineering and Automation]. Department of Industrial Electrical Engineering and Automation, Lund Institute of Technology [lucris.lub.lu.se/ws/files/5717458/587911.pdf].
• [19] Li S. et al.: Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods. Sustainability 15(11), 2023, 8952 [https://doi.org/10.3390/su15118952].
• [20] Mahfoud S. et al.: A New Hybrid Ant Colony Optimization Based PID of the Direct Torque Control for a Doubly Fed Induction Motor. World Electric Vehicle Journal 13(5), 2022, 78 [https://doi.org/10.3390/wevj13050078].
• [21] Mirjalili S., Lewis A.: The Whale Optimization Algorithm. Advances in Engineering Software 95, 2016, 51–67 [https://doi.org/10.1016/j.advengsoft.2016.01.008].
• [22] Mohamed A.: Hierarchical Control for DC Microgrids. InTech EBooks, 2016 [https://doi.org/10.5772/63986].
• [23] Muchande S., Thale S.: Hierarchical Control of a Low Voltage DC Microgrid with Coordinated Power Management Strategies. Engineering, Technology & Applied Science Research 12(1), 2022, 8045–8052 [https://doi.org/10.48084/etasr.4625].
• [24] Nadimi-Shahraki M. H et al.: A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations. Archives of Computational Methods in Engineering 30, 2023, 4113–4159 [https://doi.org/10.1007/s11831-023-09928-7].
• [25] Papadimitriou C. N. et al.: Review of Hierarchical Control in DC Microgrids. Electric Power Systems Research 122, 2015, 159–167 [https://doi.org/10.1016/j.epsr.2015.01.006].
• [26] Pedro J. O. et al.: Differential Evolution-Based PID Control of a Quadrotor System for Hovering Application. Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada, 2016, 2791–2798 [https://doi.org/10.1109/cec.2016.7744141].
• [27] Phatiphat T.: Port-Hamiltonian Formulation of Adaptive Hamiltonian PID Controller to Solve Constant Power Load Stability Issue in DC Microgrid: Control of a Fuel Cell Converter. 12th Energy Conversion Congress & Exposition - Asia (ECCE-Asia), Singapore, Singapore, 2021, 1864–1869 [https://doi.org/10.1109/ecce-asia49820.2021.9479070].
• [28] Praiselin W. J., Edward J. B.: Integrated Renewable Energy Sources with Droop Control Techniques-Based Microgrid Operation. Hybrid-Renewable Energy Systems in Microgrids, 2018, 39–60 [https://doi.org/10.1016/b978-0-08-102493-5.00003-0].
• [29] Ray N. K. et al.: Gravitational Search Algorithm for Optimal Tunning of Controller Parameters in AVR System. International Conference on Computational Intelligence for Smart Power System and Sustainable Energy (CISPSSE), Keonjhar, India, 2020, 1–6 [https://doi.org/10.1109/cispsse49931.2020.9212197].
• [30] Shah M. F. et al.: PID Control and Stability Analysis of an i th leg of a Six Degree of Freedom Machining Bed. Procedia Manufacturing 17, 2018, 927–934 [https://doi.org/10.1016/j.promfg.2018.10.146].
• [31] Shami T. M. et al.: Particle Swarm Optimization: A Comprehensive Survey. IEEE Access 10, 2022, 10031–10061 [https://doi.org/10.1109/ACCESS.2022.3142859].
• [32] Wang D. et al.: Particle Swarm Optimization Algorithm: An Overview. Soft Computing 22(2), 2017, 387–408 [https://doi.org/10.1007/s00500-016-2474-6].
• [33] Yuan Y., Yang S.: Hierarchical Control System for DC Microgrid. Advances in Intelligent Systems Research. Advances in Intelligent Systems Research 1, 2015 [https://doi.org/10.2991/lemcs-15.2015.233].
• [34] Zhang X.-L., Zhang Q.: Optimization of PID Parameters Based on Ant Colony Algorithm. International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Xi'an, China, 2021, 850–853 [https://doi.org/10.1109/icitbs53129.2021.00211].