nach oben

Electrical Engineering

Erschienen in:

27.06.2024 | Original Paper

Advancements in solar photovoltaic modelling: selective opposition-based variable weighted grey wolf optimizer with improved Newton–Raphson analysis

verfasst von: Ramachandran Thamaraiselvi, Menaga Dhanasekaran, Nagappan Sundaram Suresh

Erschienen in: Electrical Engineering | Ausgabe 1/2025

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This paper proposes a unique method for estimating three-diode photovoltaic (PV) model parameters that uses an enhanced Newton–Raphson (NR) method and the selective opposition-based grey wolf optimization (GWO) algorithm with variable weights. For PV systems to operate more effectively, it is essential that these characteristics be estimated accurately. The GWO algorithm and the NR method are used to their full potential in the suggested method to overcome the drawbacks of conventional approaches. The selective opposition mechanism improves the GWO algorithm's exploration and exploitation capabilities, making it possible to search the parameter space efficiently. Using variable weights modifies the impact of various search operators, enhancing convergence rate and solution quality. An enhanced NR (ENR) approach is added to the optimization process to increase accuracy further. The ENR technique is used to revise estimated parameter values from initial guesses iteratively. A real-world PV system dataset used in experiments shows that the suggested solution performs better than state-of-the-art methods. The outcomes demonstrate better robustness, convergence speed, and accuracy. The suggested method provides an effective and efficient method for extracting the unknown parameters of the three-diode photovoltaic model. The research findings can help with photovoltaic energy generation system design and optimization.

Vorheriger Artikel Development of hierarchical two-stage constrained spectral clustering algorithm to enhance power system distribution network resiliency under zonal attacks

Nächster Artikel Solving the cost minimization problem of optimal reactive power dispatch in a renewable energy integrated distribution system using rock hyraxes swarm optimization

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Abbassi R, Abbassi A, Heidari AA, Mirjalili S (2019) An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Convers Manag 179:362–372. https://doi.org/10.1016/j.enconman.2018.10.069CrossRefMATH

Abido MA, Khalid MS (2018) Seven-parameter PV model estimation using differential evolution. Electr Eng 100:971–981. https://doi.org/10.1007/s00202-017-0542-2CrossRefMATH

Ahmad L, Khordehgah N, Malinauskaite J, Jouhara H (2020) Recent advances and applications of solar photovoltaics and thermal technologies. Energy 207:11825. https://doi.org/10.1016/J.ENERGY.2020.118254CrossRefMATH

Akbari MA, Zare M, Azizipanah-abarghooee R et al (2022) The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems. Sci Rep. https://doi.org/10.1038/s41598-022-14338-zCrossRefMATH

Alam DF, Yousri DA, Eteiba MB (2015) Flower pollination algorithm based solar PV parameter estimation. Energy Convers Manag 101:410–422. https://doi.org/10.1016/j.enconman.2015.05.074CrossRefMATH

Andrei H, Ivanovici T, Predusca G, et al (2012) Curve fitting method for modeling and analysis of photovoltaic cells characteristics. In: 2012 IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR 2012—Proceedings, pp 307–312. https://doi.org/10.1109/AQTR.2012.6237722

Askarzadeh A, Rezazadeh A (2012) Parameter identification for solar cell models using harmony search-based algorithms. Sol Energy 86:3241–3249. https://doi.org/10.1016/j.solener.2012.08.018CrossRefMATH

Ayang A, Wamkeue R, Ouhrouche M et al (2019) Maximum likelihood parameters estimation of single-diode model of photovoltaic generator. Renew Energy. https://doi.org/10.1016/j.renene.2018.06.039CrossRefMATH

Ayyarao TSLV (2022) Parameter estimation of solar PV models with quantum-based avian navigation optimizer and Newton–Raphson method. J Comput Electron 21:1338–1356. https://doi.org/10.1007/S10825-022-01931-8/FIGURES/17CrossRefMATH

10.

Ayyarao TSLV, Kishore GI (2024) Parameter estimation of solar PV models with artificial humming bird optimization algorithm using various objective functions. Soft comput 28:3371–3392. https://doi.org/10.1007/S00500-023-08630-X/TABLES/16CrossRefMATH

11.

Ayyarao TSLV, Kumar PP (2022) Parameter estimation of solar PV models with a new proposed war strategy optimization algorithm. Int J Energy Res 46:7215–7238. https://doi.org/10.1002/ER.7629CrossRefMATH

12.

Batzelis EI, Papathanassiou SA (2016) A method for the analytical extraction of the single-diode PV model parameters. IEEE Trans Sustain Energy 7:504–512. https://doi.org/10.1109/TSTE.2015.2503435CrossRefMATH

13.

Benasla L, Belmadani A, Rahli M (2014) Spiral optimization algorithm for solving combined economic and emission dispatch. Int J Electr Power Energy Syst 62:163–174. https://doi.org/10.1016/J.IJEPES.2014.04.037CrossRefMATH

14.

Bollipo RB, Mikkili S, Bonthagorla PK (2020) Critical review on PV MPPT techniques: classical, intelligent and optimisation. IET Renew Power Gener 14:1433–1452. https://doi.org/10.1049/IET-RPG.2019.1163CrossRef

15.

Bulbul SMA, Pradhan M, Roy PK, Pal T (2018) Opposition-based krill herd algorithm applied to economic load dispatch problem. Ain Shams Eng J 9:423–440. https://doi.org/10.1016/j.asej.2016.02.003CrossRefMATH

16.

Ćalasan M, Abdel Aleem SHE, Zobaa AF (2020) On the root mean square error (RMSE) calculation for parameter estimation of photovoltaic models: a novel exact analytical solution based on Lambert W function. Energy Convers Manag 210:112716. https://doi.org/10.1016/j.enconman.2020.112716CrossRefMATH

17.

Chandrasekaran K, Thaveedhu ASR, Manoharan P, Periyasamy V (2023) Optimal estimation of parameters of the three-diode commercial solar photovoltaic model using an improved Berndt-Hall-Hall-Hausman method hybridized with an augmented mountain gazelle optimizer. Environ Sci Pollut Res. https://doi.org/10.1007/S11356-023-26447-X/METRICSCrossRef

18.

Chen X, Xu B, Mei C et al (2018) Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation. Appl Energy 212:1578–1588. https://doi.org/10.1016/j.apenergy.2017.12.115CrossRefMATH

19.

Chen Y, Sun Y, Meng Z (2018) An improved explicit double-diode model of solar cells: fitness verification and parameter extraction. Energy Convers Manag 169:345–358. https://doi.org/10.1016/J.ENCONMAN.2018.05.035CrossRefMATH

20.

Deotti LMP, Pereira JLR, da Silva Júnior IC (2020) Parameter extraction of photovoltaic models using an enhanced Lévy flight bat algorithm. Energy Convers Manag 221:113114. https://doi.org/10.1016/j.enconman.2020.113114CrossRef

21.

Dhargupta S, Ghosh M, Mirjalili S, Sarkar R (2020) Selective opposition based Grey Wolf optimization. Expert Syst Appl 151:113389. https://doi.org/10.1016/J.ESWA.2020.113389CrossRef

22.

Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst, Man, and Cybern Part B (Cybern) 26:29–41. https://doi.org/10.1109/3477.484436CrossRefMATH

23.

Elazab OS, Hasanien HM, Alsaidan I et al (2020) Parameter estimation of three diode photovoltaic model using grasshopper optimization algorithm. Energies. https://doi.org/10.3390/en13020497CrossRef

24.

El-Hameed MA, Elkholy MM, El-Fergany AA (2020) Three-diode model for characterization of industrial solar generating units using Manta-rays foraging optimizer: analysis and validations. Energy Convers Manag 219:113048. https://doi.org/10.1016/J.ENCONMAN.2020.113048CrossRefMATH

25.

Fadheel BA, Wahab NIA, Manoharan P et al (2024) A hybrid sparrow search optimized fractional virtual inertia control for frequency regulation of multi-microgrid system. IEEE Access 12:45879–45903. https://doi.org/10.1109/ACCESS.2024.3376468CrossRefMATH

26.

Gao ZM, Zhao J (2019) An improved grey Wolf optimization algorithm with variable weights. Comput Intell Neurosci. https://doi.org/10.1155/2019/2981282CrossRefMATH

27.

Gu W, Zhou B (2019) Improved grey wolf optimization based on the Quantum-behaved mechanism. In: Proceedings of 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2019, pp 1537–1540. https://doi.org/10.1109/IAEAC47372.2019.8997934

28.

Guo Z, Yang H, Liu S, Liu X (2017) Gravitational search algorithm with Gaussian mutation strategy. Int J Wirel Mob Comput. https://doi.org/10.1504/IJWMC.2017.084184CrossRefMATH

29.

Halim AH, Ismail I, Das S (2020) Performance assessment of the metaheuristic optimization algorithms: an exhaustive review. Artif Intell Rev 54(3):2323–2409. https://doi.org/10.1007/S10462-020-09906-6CrossRefMATH

30.

Hasanien HM (2015) Shuffled frog leaping algorithm for photovoltaic model identification. IEEE Trans Sustain Energy 6:509–515. https://doi.org/10.1109/TSTE.2015.2389858CrossRefMATH

31.

Hawashin D, Alkhateri MM, Alnuaimi N et al (2021) Performance evaluation of recent metaheuristic optimization algorithms for photovoltaic system parameter extraction. Int Rev Electr Eng 16(1):60–67. https://doi.org/10.15866/IREE.V16I1.18955CrossRef

32.

Huang H, Feng X, Zhou S et al (2019) A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features. BMC Bioinform. https://doi.org/10.1186/S12859-019-2771-ZCrossRefMATH

33.

Hussin F, Issabayeva G, Aroua MK (2018) Solar photovoltaic applications: opportunities and challenges. Rev Chem Eng 34:503–528. https://doi.org/10.1515/REVCE-2016-0058CrossRef

34.

Irudayaraj AXR, Wahab NIA, Veerasamy V et al (2023) Decentralized frequency control of restructured energy system using hybrid intelligent algorithm and non-linear fractional order proportional integral derivative controller. IET Renew Power Gener 17:2009–2037. https://doi.org/10.1049/RPG2.12746CrossRef

35.

Jafari M, Salajegheh E, Salajegheh J (2021) Elephant clan optimization: a nature-inspired metaheuristic algorithm for the optimal design of structures. Appl Soft Comput 113:107892. https://doi.org/10.1016/J.ASOC.2021.107892CrossRefMATH

36.

Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput 44:148–175. https://doi.org/10.1016/J.SWEVO.2018.02.013CrossRefMATH

37.

Janani C, ChittiBabu B, Krishnasamy V (2022) An accurate parameter estimation approach to modeling of solar photovoltaic module using hybrid grey wolf optimization. Optim Control Appl Methods. https://doi.org/10.1002/OCA.2917CrossRefMATH

38.

Jangir P, Buch H, Mirjalili S, Manoharan P (2021) MOMPA: Multi-objective marine predator algorithm for solving multi-objective optimization problems. Evolut Intell 2021:1–27. https://doi.org/10.1007/S12065-021-00649-ZCrossRefMATH

39.

Jervase JA, Bourdoucen H, Al-Lawati A (2001) Solar cell parameter extraction using genetic algorithms. Meas Sci Technol 12:1922–1925. https://doi.org/10.1088/0957-0233/12/11/322CrossRef

40.

Jeyafzam F, Vaziri B, Suraki MY et al (2021) Improvement of grey wolf optimizer with adaptive middle filter to adjust support vector machine parameters to predict diabetes complications. Neural Comput Appl 33:15205–15228. https://doi.org/10.1007/S00521-021-06143-Y/TABLES/21CrossRef

41.

Jordehi AR (2016) Parameter estimation of solar photovoltaic (PV) cells: a review. Renew Sustain Energy Rev 61:354–371. https://doi.org/10.1016/j.rser.2016.03.049CrossRefMATH

42.

Kang T, Yao J, Jin M et al (2018) A novel improved cuckoo search algorithm for parameter estimation of photovoltaic (PV) models. Energies 11:1060. https://doi.org/10.3390/en11051060CrossRefMATH

43.

Knypiński Ł (2021) Constrained optimization of line-start PM motor based on the gray wolf optimizer. Eksploatacja i Niezawodność 23(1):1–10. https://doi.org/10.17531/EIN.2021.1.1CrossRefMATH

44.

Kohli M, Arora S (2018) Chaotic grey wolf optimization algorithm for constrained optimization problems. J Comput Des Eng. https://doi.org/10.1016/j.jcde.2017.02.005CrossRefMATH

45.

Kumar C, Magdalin Mary D (2022) A novel chaotic-driven Tuna Swarm Optimizer with Newton-Raphson method for parameter identification of three-diode equivalent circuit model of solar photovoltaic cells/modules. Optik (Stuttg) 264:169379. https://doi.org/10.1016/J.IJLEO.2022.169379CrossRef

46.

Kumar C, Mary DM (2021) Parameter estimation of three-diode solar photovoltaic model using an improved-African vultures optimization algorithm with Newton–Raphson method. J Comput Electron 20:2563–2593. https://doi.org/10.1007/S10825-021-01812-6CrossRefMATH

47.

Kumar C, Raj TD, Premkumar M, Raj TD (2020) A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters. Optik (Stuttg). https://doi.org/10.1016/j.ijleo.2020.165277CrossRefMATH

48.

Kumari PA, Geethanjali P (2018) Parameter estimation for photovoltaic system under normal and partial shading conditions: a survey. Renew Sustain Energy Rev 84:1–11CrossRefMATH

49.

Kuppusamy M, Balaraman S (2022) An elite hybrid strategy for solar photovoltaic system based optimized cascade controller under uniform and partial shading conditions. Optim Control Appl Methods. https://doi.org/10.1002/OCA.2925CrossRefMATH

50.

Lakshmanan M, Kumar C, Jasper JS (2023) Optimal parameter characterization of an enhanced mathematical model of solar photovoltaic cell/module using an improved white shark optimization algorithm. Optim Control Appl Methods. https://doi.org/10.1002/OCA.2984MathSciNetCrossRefMATH

51.

Lipare A, Reddy Edla D, Cheruku R, Tripathi D (2020) GWO-GA based load balanced and energy efficient clustering approach for WSN. Smart Innov, Syst Technol 165:287–295. https://doi.org/10.1007/978-981-15-0077-0_29/COVERCrossRef

52.

Liu S, Feng ZK, Niu WJ et al (2019) Peak operation problem solving for hydropower reservoirs by elite-guide sine cosine algorithm with Gaussian local search and random mutation. Energies. https://doi.org/10.3390/en12112189CrossRefMATH

53.

Long W, Cai S, Jiao J et al (2020) A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models. Energy Convers Manag 203:112243. https://doi.org/10.1016/j.enconman.2019.112243CrossRefMATH

54.

Long W, Wu T, Xu M et al (2021) Parameters identification of photovoltaic models by using an enhanced adaptive butterfly optimization algorithm. Energy 229:120750. https://doi.org/10.1016/J.ENERGY.2021.120750CrossRef

55.

Louzazni M, Khouya A, Amechnoue K, Craciunescu A (2017) Parameter estimation of photovoltaic module using bio-inspired firefly algorithm. In: Proceedings of 2016 International Renewable and Sustainable Energy Conference, IRSEC 2016, pp 591–596. https://doi.org/10.1109/IRSEC.2016.7983895

56.

Luz EFP (2008) A new multi-particle collision algorithm for optimization in a high performance environment. J Comput Interdiscip Sci. https://doi.org/10.6062/JCIS.2008.01.01.0001CrossRefMATH

57.

Mathew D, Rani C, Kumar MR et al (2018) Wind-driven optimization technique for estimation of solar photovoltaic parameters. IEEE J Photovolt. https://doi.org/10.1109/JPHOTOV.2017.2769000CrossRefMATH

58.

Meng X, Liu Y, Gao X, Zhang H (2014) A New Bio-inspired Algorithm: Chicken Swarm Optimization. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 8794, pp 86–94. https://doi.org/10.1007/978-3-319-11857-4_10

59.

Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27:1053–1073. https://doi.org/10.1007/S00521-015-1920-1/TABLES/12CrossRefMATH

60.

Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007CrossRefMATH

61.

Mirjalili S, Saremi S, Mirjalili SM, Coelho LDS (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119. https://doi.org/10.1016/J.ESWA.2015.10.039CrossRefMATH

62.

Nadimi-Shahraki MH, Taghian S, Mirjalili S (2021) An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl 166:113917. https://doi.org/10.1016/J.ESWA.2020.113917CrossRefMATH

63.

Nguyen TT (2019) A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization. Energy 171:218–240. https://doi.org/10.1016/J.ENERGY.2019.01.021CrossRefMATH

64.

Oliva D, Abd El Aziz M, Ella Hassanien A (2017) Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Appl Energy 200:141–154. https://doi.org/10.1016/j.apenergy.2017.05.029CrossRefMATH

65.

Oliva D, Houssein EH, Hinojosa S (eds) (2021) Metaheuristics in Machine Learning: Theory and Applications. https://doi.org/10.1007/978-3-030-70542-8

66.

Pervez I, Antoniadis C, Massoud Y (2022) A reduced search space exploration metaheuristic algorithm for MPPT. IEEE Access 10:26090–26100. https://doi.org/10.1109/ACCESS.2022.3156124CrossRefMATH

67.

Podder AK, Roy NK, Pota HR (2019) MPPT methods for solar PV systems: a critical review based on tracking nature. IET Renew Power Gener 13:1615–1632. https://doi.org/10.1049/IET-RPG.2018.5946CrossRefMATH

68.

Premkumar M, Hashim TJT, Ravichandran S et al (2024) Optimal operation and control of hybrid power systems with stochastic renewables and FACTS devices: an intelligent multi-objective optimization approach. Alex Eng J 93:90–113. https://doi.org/10.1016/J.AEJ.2024.02.069CrossRef

69.

Premkumar M, Ibrahim AM, Kumar RM, Sowmya R (2019) Analysis and simulation of bio-inspired intelligent salp swarm MPPT method for the PV systems under partial shaded conditions. Int J Comput Digit Syst 8:2210–142. https://doi.org/10.12785/ijcds/080506CrossRef

70.

Premkumar M, Jangir P, Kumar BS et al (2022) Multi-objective grey Wolf optimization algorithm for solving real-world bldc motor design problem. Comput, Mater Contin 70:2435–2452. https://doi.org/10.32604/CMC.2022.016488CrossRefMATH

71.

Premkumar M, Jangir P, Kumar C et al (2022) Constraint estimation in three-diode solar photovoltaic model using Gaussian and Cauchy mutation-based hunger games search optimizer and enhanced Newton-Raphson method. IET Renew Power Gener 16:1733–1772. https://doi.org/10.1049/RPG2.12475CrossRef

72.

Premkumar M, Karthick K, Sowmya R (2018) A comparative study and analysis on conventional solar PV based DC-DC converters and MPPT techniques. Indones J Electr Eng Comput Sci. https://doi.org/10.11591/ijeecs.v11.i3.pp831-838CrossRefMATH

73.

Premkumar M, Kumar C, Sowmya R, Pradeep J (2021) A novel salp swarm assisted hybrid maximum power point tracking algorithm for the solar photovoltaic power generation systems. Automatika. https://doi.org/10.1080/00051144.2020.1834062CrossRefMATH

74.

Premkumar M, Kumar RS, Lakshmanan M, et al (2022c) Particle swarm optimizer with time-varying acceleration coefficients: application to maximum power point tracking for photovoltaic systems. In: 2022 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON), pp 179–184. https://doi.org/10.1109/CENTCON56610.2022.10051463

75.

Premkumar M, Jangir P, Sowmya R (2022) Parameter extraction of three-diode solar photovoltaic model using a new metaheuristic resistance–capacitance optimization algorithm and improved Newton–Raphson method. J Comput Electron 1:1–32. https://doi.org/10.1007/S10825-022-01987-6CrossRef

76.

Premkumar M, Shankar N, Sowmya R et al (2023) A reliable optimization framework for parameter identification of single-diode solar photovoltaic model using weighted velocity-guided grey wolf optimization algorithm and Lambert-W function. IET Renew Power Gener. https://doi.org/10.1049/RPG2.12792CrossRefMATH

77.

Premkumar M, Sowmya R, Umashankar S, Pradeep J (2020) An Effective Solar Photovoltaic Module Parameter Estimation Technique for Single-Diode Model. In: IOP Conference Series: Materials Science and Engineering

78.

Qais MH, Hasanien HM, Alghuwainem S (2020) Parameters extraction of three-diode photovoltaic model using computation and Harris Hawks optimization. Energy 195:117040. https://doi.org/10.1016/j.energy.2020.117040CrossRef

79.

80.

Qais MH, Hasanien HM, Alghuwainem S (2019) Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm. Appl Energy 250:109–117. https://doi.org/10.1016/j.apenergy.2019.05.013CrossRef

81.

Qais MH, Hasanien HM, Alghuwainem S, Nouh AS (2019) Coyote optimization algorithm for parameters extraction of three-diode photovoltaic models of photovoltaic modules. Energy 187:116001. https://doi.org/10.1016/j.energy.2019.116001CrossRef

82.

Ramachandran M, Mirjalili S, MalliRamalingam M et al (2022) A ranking-based fuzzy adaptive hybrid crow search algorithm for combined heat and power economic dispatch. Expert Syst Appl 197:116625. https://doi.org/10.1016/J.ESWA.2022.116625CrossRefMATH

83.

Ravi S, Premkumar M, Abualigah L (2023) Comparative analysis of recent metaheuristic algorithms for maximum power point tracking of solar photovoltaic systems under partial shading conditions. Int J Appl Power Eng 12:196–217. https://doi.org/10.11591/IJAPE.V12.I2.PP196-217CrossRefMATH

84.

Ravichandran S, Manoharan P, Jangir P, Selvarajan S (2023) Resistance–capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial engineering computation problems. Sci Rep. https://doi.org/10.1038/s41598-023-42969-3CrossRef

85.

Reis LRD, Camacho JR, Novacki DF (2017) The newton raphson method in the extraction of parameters of PV modules. Renew Energy Power Qual J 1(15):634–639. https://doi.org/10.24084/repqj15.416CrossRefMATH

86.

Rezk H, Fathy A (2017) A novel optimal parameters identification of triple-junction solar cell based on a recently meta-heuristic water cycle algorithm. Solar Energy 157:. https://doi.org/10.1016/j.solener.2017.08.084

87.

Ridha HM, Hizam H, Gomes C et al (2021) Parameters extraction of three diode photovoltaic models using boosted LSHADE algorithm and Newton Raphson method. Energy 224:120136. https://doi.org/10.1016/j.energy.2021.120136CrossRef

88.

Ridha HM, Hizam H, Mirjalili S et al (2022) On the problem formulation for parameter extraction of the photovoltaic model: novel integration of hybrid evolutionary algorithm and Levenberg Marquardt based on adaptive damping parameter formula. Energy Convers Manag 256:115403. https://doi.org/10.1016/J.ENCONMAN.2022.115403CrossRef

89.

Ridha HM, Hizam H, Mirjalili S et al (2022) Zero root-mean-square error for single- and double-diode photovoltaic models parameter determination. Neural Comput Appl 34:11603–11624. https://doi.org/10.1007/S00521-022-07047-1/FIGURES/4CrossRefMATH

90.

Rodríguez L, Castillo O, Soria J et al (2017) A fuzzy hierarchical operator in the grey wolf optimizer algorithm. Appl Soft Comput 57:315–328. https://doi.org/10.1016/J.ASOC.2017.03.048CrossRefMATH

91.

Sahoo SK, Premkumar M, Saha AK et al (2023) Multi-objective quasi-reflection learning and weight strategy-based moth flame optimization algorithm. Neural Comput Appl. https://doi.org/10.1007/S00521-023-09234-0/TABLES/10CrossRefMATH

92.

Sahoo SK, Saha AK, Houssein EH et al (2024) An arithmetic and geometric mean-based multi-objective moth-flame optimization algorithm. Cluster Comput. https://doi.org/10.1007/S10586-024-04301-0/TABLES/23CrossRefMATH

93.

Sandrolini L, Artioli M, Reggiani U (2010) Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis. Appl Energy 87:442–451. https://doi.org/10.1016/j.apenergy.2009.07.022CrossRefMATH

94.

Saxena A, Sharma A, Shekhawat S (2020) Parameter extraction of solar cell using intelligent grey wolf optimizer. Evol Intell. https://doi.org/10.1007/s12065-020-00499-1CrossRefMATH

95.

Schulman J, Levine S, Moritz P, et al (2015) Trust region policy optimization. In: 32nd International Conference on Machine Learning, ICML 2015, vol 3, pp 1889–1897. https://doi.org/10.48550/arxiv.1502.05477

96.

Sowmya R, Premkumar M, Jangir P (2024) Newton-Raphson-based optimizer: a new population-based metaheuristic algorithm for continuous optimization problems. Eng Appl Artif Intell 128:107532. https://doi.org/10.1016/J.ENGAPPAI.2023.107532CrossRefMATH

97.

Tadepalli Y, Kollati M, Kuraparthi S et al (2021) Content-based image retrieval using Gaussian-Hermite moments and firefly and grey wolf optimization. CAAI Trans Intell Technol 6:135–146. https://doi.org/10.1049/CIT2.12040CrossRef

98.

Tripathy M, Kumar M, Sadhu PK (2017) Photovoltaic system using Lambert W function-based technique. Solar Energy. https://doi.org/10.1016/j.solener.2017.10.007CrossRefMATH

99.

Venkateswari R, Rajasekar N (2021) Review on parameter estimation techniques of solar photovoltaic systems. Int Trans Electr Energy Syst 31:e13113. https://doi.org/10.1002/2050-7038.13113CrossRefMATH

100.

Wang J, Lin D, Zhang Y, Huang S (2022) An adaptively balanced grey wolf optimization algorithm for feature selection on high-dimensional classification. Eng Appl Artif Intell 114:105088. https://doi.org/10.1016/J.ENGAPPAI.2022.105088CrossRefMATH

101.

Wei T, Yu F, Huang G, Xu C (2019) A particle-swarm-optimization-based parameter extraction routine for three-diode lumped parameter model of organic solar cells. IEEE Electron Device Lett 40:1511–1514. https://doi.org/10.1109/LED.2019.2926315CrossRefMATH

102.

Wu TQ, Yao M, Yang JH (2016) Dolphin swarm algorithm. Front Inf Technol Electron Eng 17:717–729. https://doi.org/10.1631/FITEE.1500287/TABLES/9CrossRefMATH

103.

Wu Z, Yu D, Kang X (2017) Parameter identification of photovoltaic cell model based on improved ant lion optimizer. Energy Convers Manag 151:107–115. https://doi.org/10.1016/j.enconman.2017.08.088CrossRefMATH

104.

Yousri D, Abd M, Oliva D et al (2020) Reliable applied objective for identifying simple and detailed photovoltaic models using modern metaheuristics: comparative study. Energy Convers Manag 223:113279. https://doi.org/10.1016/j.enconman.2020.113279CrossRefMATH

105.

Yu X, Jiang N, Wang X, Li M (2023) A hybrid algorithm based on grey wolf optimizer and differential evolution for UAV path planning. Expert Syst Appl 215:119327. https://doi.org/10.1016/J.ESWA.2022.119327CrossRefMATH

106.

Zhang H, Heidari AA, Wang M et al (2020) Orthogonal Nelder-Mead moth flame method for parameters identification of photovoltaic modules. Energy Convers Manag. https://doi.org/10.1016/j.enconman.2020.112764CrossRefMATH

Titel: Advancements in solar photovoltaic modelling: selective opposition-based variable weighted grey wolf optimizer with improved Newton–Raphson analysis
verfasst von: Ramachandran Thamaraiselvi
Menaga Dhanasekaran
Nagappan Sundaram Suresh
Publikationsdatum: 27.06.2024
Verlag: Springer Berlin Heidelberg
Erschienen in: Electrical Engineering / Ausgabe 1/2025
Print ISSN: 0948-7921
Elektronische ISSN: 1432-0487
DOI: https://doi.org/10.1007/s00202-024-02547-w

A novel hybrid algorithm based on optimal size and location of photovoltaic with battery energy storage systems for voltage stability enhancement

Original Paper

Advancing microgrid power quality: integration of GRU-based control in PV-UPQC systems

Original Paper

Hardware implementation of improved transformer-less grid-connected pv inverter topologies with constant common mode voltage and enhanced efficiency

Original Paper

A novel dual-layer cascaded torque control mechanism for the improved drivetrain performance for electric vehicles

Original Paper

Study on transient characteristics of secondary arc of AC/DC hybrid transmission lines on the same tower

Original Paper

A reference current control strategy based on Sogi and FBD method for shunt active power filter

Original Paper

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

A novel hybrid algorithm based on optimal size and location of photovoltaic with battery energy storage systems for voltage stability enhancement

Advancing microgrid power quality: integration of GRU-based control in PV-UPQC systems

Hardware implementation of improved transformer-less grid-connected pv inverter topologies with constant common mode voltage and enhanced efficiency

A novel dual-layer cascaded torque control mechanism for the improved drivetrain performance for electric vehicles

Study on transient characteristics of secondary arc of AC/DC hybrid transmission lines on the same tower

A reference current control strategy based on Sogi and FBD method for shunt active power filter