Stability of metaheuristic PID controllers in photovoltaic dc microgrids
Article Sidebar
Open full text
Issue Vol. 15 No. 1 (2025)
-
Statistical reliability of decisions on controlled process faults
Yevhen Volodarskyi, Oleh Kozyr, Zygmunt Warsza5-9
-
Pulse chaotic generator based a classical Chua’s circuit
Volodymyr Rusyn, Andrii Samila, Bogdan Markovych, Aceng Sambas, Christos Skiadas, Milan Guzan10-14
-
Stability of metaheuristic PID controllers in photovoltaic dc microgrids
Elvin Yusubov, Lala Bekirova15-21
-
Integrating numerical simulation and experimental data for enhanced structural health monitoring of bridges
Om Narayan Singh, Kaushik Dey22-26
-
Application of multi-agent programming for modeling the viscosity state of mash in alcohol production
Larysa Gumeniuk, Ludmyla Markina, Viktor Satsyk, Pavlo Humeniuk, Anton Lashch27-32
-
A stochastic interval algebra for smart factory processes
Piotr Dziurzanski, Konrad Kabala, Agnieszka Konrad33-38
-
Advancements in solar panel maintenance: a review of IoT-integrated automatic dust cleaning systems
Balamurugan Rangaswamy, Ramasamy Nithya39-44
-
Modified cosine-quadratic reflectance model
Oleksandr Romanyuk, Volodymyr Lytvynenko, Yevhen Zavalniuk45-48
-
Comparative analysis of lithium-iron-phosphate and sodium-ion energy storage devices
Huthaifa A. Al_Issa, Mohamed Qawaqzeh, Lina Hani Hussienat, Ruslan Oksenych, Oleksandr Miroshnyk, Oleksandr Moroz, Iryna Trunova, Volodymyr Paziy, Serhii Halko, Taras Shchur49-54
-
Investigation of DC-AC converter with microcontroller control of inverter frequency
Anatolii Tkachuk, Mykola Polishchuk, Liliia Polishchuk, Serhii Kostiuchko, Serhii Hryniuk, Liudmyla Konkevych55-61
-
Mathematical apparatus for finding the optimal configuration secure communication network with a specified number of subscribers
Volodymyr Khoroshko, Yuliia Khokhlachova, Oleksandr Laptiev, Al-Dalvash Ablullah Fowad62-66
-
Critical cybersecurity aspects for improving enterprise digital infrastructure protection
Roman Kvуetnyy, Volodymyr Kotsiubynskyi, Serhii Husak, Yaroslav Movchan, Nataliia Dobrovolska, Sholpan Zhumagulova, Assel Aitkazina67-72
-
Modification of the Peterson algebraic decoder
Dmytro Mogylevych, Iryna Kononova, Liudmyla Pogrebniak, Kostiantyn Lytvyn, Igor Gyrenko73-78
-
Development of a model for calculating the dilution of precision coefficients of the global navigation system at a given point in space
Oleksandr Turovsky, Nazarii Blazhennyi, Roman Vozniak, Yana Horbachova, Kostiantyn Horbachov, Nataliia Rudenko79-87
-
LLM based expert AI agent for mission operation management
Sobhana Mummaneni, Syama Sameera Gudipati, Satwik Panda88-94
-
Review of operating systems used in unmanned aerial vehicles
Viktor Ivashko, Oleh Krulikovskyi, Serhii Haliuk, Andrii Samila95-100
-
Optimization of machine learning methods for de-anonymization in social networks
Nurzhigit Smailov, Fatima Uralova, Rashida Kadyrova, Raiymbek Magazov, Akezhan Sabibolda101-104
-
Robust deepfake detection using Long Short-Term Memory networks for video authentication
Ravi Kishan Surapaneni, Hameed Syed, Harshitha Kakarala, Venkata Sai Srikar Yaragudipati105-108
-
Regional trending topics mining from real time Twitter data for sentiment, context, network and temporal analysis
Mousumi Hasan, Mujiba Shaima, Quazi Saad ul Mosaher109-116
-
Model development to improve the predictive maintenance reliability of medical devices
Khalid Musallam Alahmadi, Essam Rabea Ibrahim Mahmoud, Fitrian Imaduddin117-124
-
Explainable artificial intelligence for detecting lung cancer
Vinod Kumar R S, Bushara A R, Abubeker K M, Smitha K M, Abini M A, Jubaira Mammoo, Bijesh Paul125-130
-
Design and implementation of a vein detection system for improved accuracy in blood sampling
Omar Boutalaka, Achraf Benba, Sara Sandabad131-134
-
Metrological feature for determining the concentration of cholesterol, triglycerides, and phospholipids for psoriasis detection
Ivan Diskovskyi, Yurii Kachurak, Orysya Syzon, Marta Kolishetska, Bogdan Pinaiev, Oksana Stoliarenko135-138
-
Development of a mobile application for testing fine motor skills disorders
Marko Andrushchenko, Karina Selivanova, Oleg Avrunin, Alla Kraievska, Orken Mamyrbayev, Kymbat Momynzhanova139-143
-
Artificial intelligence in education: ChatGPT-based simulations in teachers’ preparation
Marina Drushlyak, Tetiana Lukashova, Volodymyr Shamonia, Olena Semenikhina144-152
-
CKSD: Comprehensive Kurdish-Sorani database
Jihad Anwar Qadir, Samer Kais Jameel, Wshyar Omar Khudhur, Kamaran H. Manguri153-156
Archives
-
Vol. 15 No. 3
2025-09-30 24
-
Vol. 15 No. 2
2025-06-27 24
-
Vol. 15 No. 1
2025-03-31 26
-
Vol. 14 No. 4
2024-12-21 25
-
Vol. 14 No. 3
2024-09-30 24
-
Vol. 14 No. 2
2024-06-30 24
-
Vol. 14 No. 1
2024-03-31 23
-
Vol. 13 No. 4
2023-12-20 24
-
Vol. 13 No. 3
2023-09-30 25
-
Vol. 13 No. 2
2023-06-30 14
-
Vol. 13 No. 1
2023-03-31 12
-
Vol. 12 No. 4
2022-12-30 16
-
Vol. 12 No. 3
2022-09-30 15
-
Vol. 12 No. 2
2022-06-30 16
-
Vol. 12 No. 1
2022-03-31 9
-
Vol. 11 No. 4
2021-12-20 15
-
Vol. 11 No. 3
2021-09-30 10
-
Vol. 11 No. 2
2021-06-30 11
-
Vol. 11 No. 1
2021-03-31 14
Main Article Content
DOI
Authors
Abstract
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.
Keywords:
References
[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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: https://doi.org/10.1109/CISPSSE49931.2020.9212197
[30] Shah M. F. et al.: PID Control and Stability Analysis of an ith 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: 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]. DOI: https://doi.org/10.1109/ICITBS53129.2021.00211
Article Details
Abstract views: 188

