PSO tuned interval type-2 fuzzy logic for load frequency control of two-area multi-source interconnected power system.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
30 May 2023
30 May 2023
Historique:
received:
10
01
2023
accepted:
18
05
2023
medline:
31
5
2023
pubmed:
31
5
2023
entrez:
30
5
2023
Statut:
epublish
Résumé
Nowadays, most of modern power systems integrate concentrated renewable energy resources power plants like solar and wind parks in addition to central conventional plants. The output power from these concentrated renewable energy resources varies continuously according to weather conditions like solar irradiance value or wind speed and direction, the variation for their output power may be in mega watts. In this work, Robust secondary load frequency controller (LFC) based on one of artificial intelligent technique which called interval type-2 fuzzy logic controller (IT2FLC) has been proposed for two-area multi-source interconnected power system with central solar park power plants in each area while considering non-linearities in the power system. IT2FLC has accommodated vagueness, distortions and imprecision for the power system input signals which caused by weather fluctuations and system non-linearities. In addition to LFC, another controller based also on IT2FLC has been proposed to control the output power from the central solar parks in each area of generation during cloudy periods instead of maximum power point tracking method (MPPT) in order to enhance the stability for the power system during disturbance periods. In order to enhance the performance of the proposed LFC, particle swarm optimization technique (PSO) has been utilized to optimize the proposed LFC gains to minimize the steady state error, over/under shooting value, settling time and system oscillation for the investigated power system frequency. The performance and the superiority of the proposed PSO tuned IT2FLC is evaluated and compared with another LFC based on PSO tuned cascaded PID controller while applying severe demand load and solar irradiance changes. the simulation has been carried out using matlab/simulink program.
Identifiants
pubmed: 37253774
doi: 10.1038/s41598-023-35454-4
pii: 10.1038/s41598-023-35454-4
pmc: PMC10229559
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8724Informations de copyright
© 2023. The Author(s).
Références
Bencs, P., Al-Ktranee, M. & Mészáros, K. M. Effects of solar panels on electrical networks. Anal. Tech. Szeged. 14(1), 50–60 (2020).
doi: 10.14232/analecta.2020.1.50-60
Wolfe, P. R. Utility-Scale Solar Power. In McEvoy's Handbook of Photovoltaics, 3rd edn. (2018)
Fathabad, A. M., Cheng, J., Pan, K. & Qiu, F. Data-driven planning for renewable distributed generation in distribution systems. IEEE Trans. Power Syst. 35(6), 4357–4368 (2020).
doi: 10.1109/TPWRS.2020.3001235
da Silva, P. P., Dantas, G., Pereira, G. I., Câmara, L. & De Castro, N. J. Photovoltaic distributed generation – An international review on diffusion, support policies, and electricity sector regulatory adaption. Renew. Sustain. Energy Rev. 103, 30–39 (2019).
doi: 10.1016/j.rser.2018.12.028
Musanga, L. M., Barasa, W. H. & Maxwell, M. The effect of irradiance and temperature on the performance of monocrystalline silicon solar module in Kakamega. Phys. Sci. Int. J. 19(4), 1–9 (2018).
doi: 10.9734/PSIJ/2018/44862
Ghosh, J., Seetharaman, A. & Maddulety, K. The major factors influencing on the growth of solar energy usage in India. Indian Econ. J. 68(1), 122–128 (2020).
doi: 10.1177/0019466220953544
Gersdorf, T., Hensley, Hertzke, P., Schaufuss, P., Tschiesner, A. The road ahead for e-mobility. McKinsey. (2020).
Mastoi, M. S. et al. An in-depth analysis of electric vehicle charging station infrastructure, policy implications, and future trends. Energy Rep. 8, 11504–11529 (2022).
doi: 10.1016/j.egyr.2022.09.011
Lokanatha, M. & Vasu, K. Load frequency control of two area power system using PID controller. Int. J. Eng. Res. Technol. (IJERT) 3(11), 687–692 (2014).
Gupta, N. K., Kar, M. K. & Singh, A. K. Load frequency control of two-area power system by using 2 degree of freedom controller designed with the help of firefly algorithm. In Control Applications in Modern Power System. 57–64 (2021).
Alzaher, A. N. A., Shafei, M. A. R. & Ibrahim, D. K. Enhancing Load Frequency Control of multi-area multi-power sources system with renewable units and including nonlinearities. Indones. J. Electr. Eng. Comput. Sci. 19(1), 108–118 (2020).
Farahani, M., Ganjefar, S. & Alizadeh, M. PID controller adjustment using chaotic ptimization algorithm for multi-area load frequency control. IET Control Theory Appl. 6(13), 1984–1992 (2012).
doi: 10.1049/iet-cta.2011.0405
Shafei, M. A. R., Ibrahim, D. K. & Bahaa, M. Application of PSO tuned fuzzy logic controller for LFC of two-area power system with redox flow battery and PV solar park. Ain Shams Eng. J. 13(5), 101710 (2022).
doi: 10.1016/j.asej.2022.101710
Chidambaram, I. A. & Paramasivam, B. Optimized load-frequency simulation in restructured power system with Redox Flow Batteries and Interline Power Flow Controller. Int. J. Electr. Power Energy Syst. 50, 9–24 (2013).
doi: 10.1016/j.ijepes.2013.02.004
Soliman, A. M. A., Eldin, M. B. & Mehanna, M. A. Application of WOA tuned type-2 FLC for LFC of two area power system with RFB and solar park considering TCPS in interline. IEEE Access 10, 112007–112018 (2022).
doi: 10.1109/ACCESS.2022.3215530
Abazari, A., Dozein, M. G., Monsef, H. & Wu, B. Wind turbine participation in micro-grid frequency control through self-tuning, adaptive fuzzy droop in de-loaded area. IET Smart Grid. 2(2), 301–308 (2019).
doi: 10.1049/iet-stg.2018.0095
Abazari, A., Babaei, M., Muyeen, S. M. & Kamwa, I. Learning adaptive fuzzy droop of PV contribution to frequency excursion of hybrid micro-grid during parameters uncertainties. Electr. Power Energy Syst. 123, 106305 (2020).
doi: 10.1016/j.ijepes.2020.106305
Abhijith Pappachen, A. Peer Fathima, NERC’s control performance standards based load frequency controller for a multi area deregulated power system with ANFIS approach. Ain Shams Eng. J. 9(4), 2399–2414 (2018).
doi: 10.1016/j.asej.2017.05.006
Dongrui, Wu. On the fundamental differences between interval type-2 and type-1 fuzzy logic controllers. IEEE Trans. Fuzzy Syst. 20(5), 832–848 (2012).
doi: 10.1109/TFUZZ.2012.2186818
Azar, A. Overview of type-2 fuzzy logic systems. Int. J. Fuzzy Syst. Appl. (IJFSA) 2, 1–28 (2012).
Kennedy, J. & Eberhart, R. Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. 5, 1942–1948 (1995).
doi: 10.1109/ICNN.1995.488968
Mezura-Montes, E. & Coello, C. A. C. Constraint-handling in nature-inspired numerical optimization: Past, present and future. Swarm Evol. Comput. 1(4), 173–194 (2011).
doi: 10.1016/j.swevo.2011.10.001
Ahmed, G. Gad, particle swarm optimization algorithm and its applications: A systematic review. Arch. Comput. Methods Eng. 29, 2531–2561 (2022).
doi: 10.1007/s11831-021-09694-4
Sasaki, T., Shigematsu, T. & Deguchi, H. E. Evaluation study about Redox flow battery response and its modeling. IEEJ Trans. Power Energy 122(4), 554–560 (2002).
doi: 10.1541/ieejpes1990.122.4_554
Mendel, J., Hagras, H., Tan, W.-W., Melek, W. W., Ying, H. Introduction to Type-2 Fuzzy Logic Control: Theory and Applications (IEEE Press, John Wiley & Sons, Hoboken, New Jersey, 2014).
Mirjalili, S. A simple implementation of Particle Swarm Optimization (PSO) Algorithm ( https://www.mathworks.com/matlabcentral/fileexchange/67429-a-simple-implementation-of-particle-swarm-optimization-pso-algorithm ), MATLAB Central File Exchange. Retrieved January 9, 2023.