DOI: 10.15507/2658-4123.035.202501.333-354
Combination of Evolutionary Algorithms and Direct Search Approaches for Improving the Dynamic Performance of Grid Connected Solar Power System
Amit Verma
Research Scholar, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology (Deoria Road, Gorakhpur 273016, India), ORCID: https://orcid.org/0000-0002-1591-1523, This email address is being protected from spambots. You need JavaScript enabled to view it.
Prabhakar Tiwari
PhD., Professor, Department of Electrical Engineering, Madan Mohan Malaviya University of Technology (Deoria Road, Gorakhpur 273016, India), ORCID: https://orcid.org/0000-0003-3923-9126, This email address is being protected from spambots. You need JavaScript enabled to view it.
Desh D. Sharma
PhD., Associate Professor, Department of Electrical Engineering, Mahatma Jyotiba Phule Rohilkhand University (Pilibhit Bypass Road, Bareilly 243006, India), ORCID: https://orcid.org/0000-0003-4512-4878, This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Introduction. The Grid Connected Photovoltaic System comprises two fundamental control loops: an external loop responsible for overseeing the DC link voltage, and an internal control loop that regulates the inverter current. The primary element of any control loop is the proportional-integral controller and determining the appropriate gains for this controller is a difficult issue.
Aim of the Study. The study aimed to adjust the gains of the PI controllers in both static and dynamic irradiance scenarios for improving DC-link voltage by novel hybrid optimization method named Genetic Algorithm- Simulated Annealing and Genetic Algorithm-Pattern search.
Materials and Methods. In this paper we use two hybrid optimizations techniques called Genetic Algorithm- simulated Annealing and Genetic Algorithm- Pattern Search to adjust the gains of the PI controllers in both static and dynamic irradiance scenarios for improving DC-link voltage.
Results. Finally, this study presents comparison of DC-link voltage with six cases with manual tuning of PI controller, as well as PI controller by Genetic Algorithm- simulated Annealing, Genetic Algorithm- Pattern Search, Genetic Algorithm, Simulated Annealing and Pattern Search. The comparison showed by using Genetic Algorithm-Simulated Annealing, peak overshoot in DC-link voltage is 829.3 V while peak overshoot in DC-link voltage is 1 052 V when DC-link voltage is controlled by manual tuning of PI as well as significant reduction in peak time and settling time in DC-link voltage.
Discussion and Conclusion. The results achieved to strengthen the DC-link voltage under both static and dynamic irradiance conditions enable the sustaining of a constant DC-link voltage, which is essential for grid-connected photovoltaic systems. The comparison showed by using Genetic Algorithm- Simulated Annealing, peak overshoot in DC-link voltage is 829.3 V while peak overshoot in DC-link voltage is 1 052 V when DC-link voltage is controlled by manual tuning of PI as well as significant reduction in peak time and settling time in DC-link voltage.
Keywords: genetic algorithm, simulated annealing, pattern search, hybridized genetic algorithm and simulated annealing
Conflict of interest: The authors declare that there is no conflict of interest.
For citation: Verma A., Tiwari P., Sharma D. D. Combination of Evolutionary Algorithms and Direct Search Approaches for Improving the Dynamic Performance of Grid Connected Solar Power System. Engineering Technologies and Systems. 2025;35(2):333–354. https://doi.org/10.15507/2658-4123.035.202502.333-354
Authors contribution:
А. Verma – developing the study methodology, creating the models, preparing the manuscript: critical analysis of the manuscript and of comments and corrections made by the members of the research group during the pre-publication and post-publication stages.
P. Tiwari – developing the study methodology, creating the models, carrying out the study, including performing experiments or collecting evidence; preparing the manuscript: visualizing the study the findings and data obtained.
D. D. Sharma – formulating the study idea, aims and objectives, carrying out the study including performing experiments and collecting evidence.
All authors have read and approved the final manuscript.
Submitted 19.09.2024;
revised 07.11.2024;
accepted 15.11.2024
REFERENCES
- Aouchiche N. Meta-Heuristic Optimization Algorithms Based Direct Currentand DC Link Voltage Controllers for Three-Phase Grid Connected Photovoltaic Inverter. Solar Energy. 2020;207:683–692. https://doi.org/10.1016/j.solener.2020.06.086
- Zakzouk N.E., Abdelsalam A.K., Helal A.A., Williams B.W. PV Single-Phase Grid-Connected Converter: DC-Link Voltage Sensorless Prospective. IEEE Journal of Emerging and Selected Topics in Power Electronics. 2017;5(1):526–546. https://doi.org/10.1109/JESTPE.2016.2637000
- Attia M.A. Optimized Controllers for Enhancing Dynamic Performance of PV Interface System. Journal of Electrical Systems and Information Technology. 2018;5(1):1–10. https://doi.org/10.1016/J.JESIT.2018.01.003
- Elazab O.S., Debouza M., Hasanien H.M., Muyeen S.M., Al-Durra A. Salp Swarm Algorithm-Based Optimal Control Scheme for LVRT Capability Improvement of Grid-Connected Photovoltaic Power Plants: Design and Experimental Validation. IET Renewable Power Generation. 2020;14(4):591–599. https://doi.org/10.1049/IET-RPG.2019.0726
- Kumar C.S., Puttamadappa C., Chandrashekar Y.L. Power Quality Enhancement in Grid-Connected PV Structure Using Z Source Inverter and Seagull Optimization Algorithm. AIP Conference Proceedings. 2022;2640(1): 020013. https://doi.org/10.1063/5.0110515
- Pradhan R. Design of Observer-Based Robust Double Integral Sliding Mode Controller for Grid-Connected PV System. Lecture Notes in Electrical Engineering. 2023;1039:429–449. https://doi.org/10.1007/978-981-99-2066-2_20
- Vanaja N., Kumar N.S. Power Quality Enhancement Using Evolutionary Algorithms in Grid-Integrated PV Inverter. Journal of Electrical Engineering and Technology. 2023;18:3615–3633. https://doi.org/10.1007/S42835-023-01443-W
- Bouali Y., Imarazene K., Berkouk E.M. Total Harmonic Distortion Optimization of Multilevel Inverters Using Genetic Algorithm: Experimental Test on NPC Topology with Self-Balancing of Capacitors Voltage Using Multilevel DC–DC Converter. Arabian Journal for Scienceand Engineering. 2023;(5). Available at: https://www.springerprofessional.de/en/total-harmonic-distortion-optimization-of-multilevel-inverters-u/23507302 (accessed 29.10.2024).
- Mateen A., Sarwar M., Hussain B., Abid M. Optimized Dual Loop Control Strategy for Grid-Connected Interleaved Inverters. 2022 19th International Bhurban Conference on Applied Sciences and Technology (IBCAST). 2022. https://doi.org/10.1109/IBCAST54850.2022.9990109
- Roslan M.F., Al-Shetwi A.Q., Hannan M.A., Ker P.J., Zuhdi A.W.M. Particle Swarm Optimization Algorithm-Based PI Inverter Controller for aGrid-Connected PV System. PLOS One. 2020;16(10):e0243581. https://doi.org/10.1371/JOURNAL.PONE.0243581
- Essaghir S., Benchagra M., El Barbri N. Comparative Study of Three Phase Grid Connected Photovoltaic System Using PI, PR and Fuzzy Logic PI Controller with Harmonic Analysis. Lecture Notes in Electrical Engineering. 2020;624. https://doi.org/10.1007/978-3-030-36475-5_5
- Allam D., Mohamed H., Al-Gabalawy M., Eteiba M.B. Optimization of Voltage Source Inverter’s Controllers Using Salp Swarm Algorithm in Grid Connected Photovoltaic System. 2019 21st International Middle East Power Systems Conference (MEPCON). 2019. https://doi.org/10.1109/MEPCON47431.2019.9008199
- Uslu M.F., Uslu S., Bulut F. An Adaptive Hybrid Approach: Combining Genetic Algorithm and ant Colony Optimization for Integrated Process Planning and Scheduling. Applied Computing and Informatics. 2022;18(1–2):101–112. https://doi.org/10.1016/J.ACI.2018.12.002
- Kelner V., Capitanescu F., Léonard O., Wehenkel L. A Hybrid Optimization Technique Coupling an Evolutionary and a Local Search Algorithm. Journal of Computational and Applied Mathematics. 2008;215(2):448–456. https://doi.org/10.1016/J.CAM.2006.03.048
- Mousakazemi S.M.H. Comparison of the Error-Integral Performance Indexes in aGA-Tuned PID Controlling System of a PWR-Type Nuclear Reactor Point-Kinetics Model. Progress in Nuclear Energy. 2021;132:103604. https://doi.org/10.1016/J.PNUCENE.2020.103604
- Wei H., Li S., Jiang H., Hu J., Hu J. Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion. Applied Sciences. 2018;8(12):2621. https://doi.org/10.3390/APP8122621
- Shahidul Islam M., Rafiqul Islam M. A Hybrid Framework Based on Genetic Algorithm and Simulated Annealing for RNA Structure Prediction with Pseudoknots. Journal of King Saud University – Computer and Information Sciences. 2022;34(3):912–922. https://doi.org/10.1016/J.JKSUCI.2020.03.005
- Al-Othman A.K., Ahmed N.A., Alsharidah M.E., Almekhaizim H.A. A Hybrid Real Coded Genetic Algorithm– Pattern Search Approach for Selective Harmonic Elimination of PWM AC/AC Voltage Controller. International Journal of Electrical Power &Energy Systems. 2013;44(1):123–133. https://doi.org/10.1016/J.IJEPES.2012.07.034
- Zhang Y.D., Wu L.N., Huo Y.K., Wang S.H. ANovel Global Optimization Method– Genetic Pattern Search. Applied Mechanics and Materials. 2011;44–47:3240–3244. https://doi.org/10.4028/WWW.SCIENTIFIC.NET/AMM.44-47.3240
- Ali W., Li Y., Ahmed N., Ali W., Kashif M. A Novel Application of Pattern Search Algorithm for Efficient Estimation of Channel State Information in MIMO Network. Wireless Personal Communication. 2021;116:325–340. https://doi.org/10.1007/s11277-020-07717-0
- Pandey H.M., Rajput M., Mishra V. Performance Comparison of Pattern Search, Simulated Annealing, Genetic Algorithm and Jaya Algorithm. Advances Intelligent Systems and Computing. 2017;542:377–384. https://doi.org/10.1007/978-981-10-3223-3_36
- Chen S.-M., Chien C.-Y. Solving the Traveling Salesman Problem Based on the Genetic Simulated Annealing ant Colony System with Particle Swarm Optimization Techniques. Expert Systems with Applications. 2011;38(12):14439–14450. https://doi.org/10.1016/J.ESWA.2011.04.163
- Tammam M.A., Aboelela M.A.S., Moustafa M.A., Seif A.E.A. A Multi-Objective Genetic Algorithm Based PID Controller for Load Frequency Control of Power Systems. International Journal of Emerging Technology and Advanced Engineering. 2013;3(12):463–467. Available at: https://clck.ru/3M7EtT (accessed 29.10.2024).
- Lambora A., Gupta K., Chopra K. Genetic Algorithm-A Literature Review. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). 2019. https://doi.org/10.1109/COMITCON.2019.8862255
This work is licensed under a Creative Commons Attribution 4.0 License.