nach oben

Soft Computing

Erschienen in:

15.05.2021 | Application of soft computing

Maximum power point tracking in photovoltaic systems using indirect adaptive fuzzy robust controller

verfasst von: Hadi Delavari, Morteza Zolfi

Erschienen in: Soft Computing | Ausgabe 16/2021

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

A photovoltaic system is used to produce energy that is dependent on environmental conditions such as irradiance, temperature, and the load attached to it. Due to the nonlinear characteristics, parameter uncertainties and load disturbance of photovoltaic (PV) systems and the problem of low efficiency due to the variation of environmental conditions, the maximum power point tracking method is required to extract the maximum power from the PV system. So, in this paper a novel indirect adaptive fuzzy fractional-order sliding mode is proposed to tackle this challenge. This method is based on a two-loop method. The first loop is designed to seek the maximum power point, and the second loop is designed to track the maximum power point. At first, a novel fractional-order sliding surface is proposed. Then, chattering in control signal is removed using a continuous function. In this paper, an indirect adaptive fuzzy system is used to estimate the unknown function of the system and adaptation laws are obtained based on the Lyapunov theory. The performance of the photovoltaic system is investigated by applying the proposed method, conventional sliding mode controller, integral sliding mode controller and backstepping sliding mode controller. Simulation results indicate the effectiveness of the proposed method.

Vorheriger Artikel Design and development of an adaptive-torque-based proportional-integral-derivative controller for a two-legged robot

Nächster Artikel An adaptive fuzzy inference approach for color image steganography

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 "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

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

Nur mit Berechtigung zugänglich

Ali SH (2012) Miner for OACCR: case of medical data analysis in knowledge discovery. In: 2012 6th international conference on sciences of electronics, technologies of information and telecommunications (SETIT). IEEE. https://doi.org/10.1109/setit.2012.6482043

Al-Janabi S, Alkaim AF (2019) A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation. Soft Comput 24(1):555–569. https://doi.org/10.1007/s00500-019-03972-xCrossRef

Al-Janabi S, Mahdi MA (2019) Evaluation prediction techniques to achievement an optimal biomedical analysis. Int J Grid Util Comput 10(5):512–527. https://doi.org/10.1504/ijguc.2019.102021CrossRef

Al-Janabi S, Mohammad M, Al-Sultan A (2019) A new method for prediction of air pollution based on intelligent computation. Soft Comput 24(1):661–680. https://doi.org/10.1007/s00500-019-04495-1CrossRef

Al-Janabi S, Alkaim AF, Adel Z (2020) An innovative synthesis of deep learning techniques (DCapsNet & DCOM) for generation electrical renewable energy from wind energy. Soft Comput 24(14):10943–10962. https://doi.org/10.1007/s00500-020-04905-9CrossRef

Alkaim AF, Al-Janabi S (2020) Multi objectives optimization to gas flaring reduction from oil production, in big data and networks technologies. pp 117–139

Amin F et al (2018a) Triangular cubic linguistic hesitant fuzzy aggregation operators and their application in group decision making. J Intell Fuzzy Syst 34(4):2401–2416. https://doi.org/10.3233/jifs-171567CrossRef

Amin F, Fahmi A, Abdullah S (2018b) Dealer using a new trapezoidal cubic hesitant fuzzy TOPSIS method and application to group decision-making program. Soft Comput 23(14):5353–5366. https://doi.org/10.1007/s00500-018-3476-3CrossRefMATH

Asma Z, Karim D, Tarak D (2016) Maximum power point tracking of photovoltaic systems based on fast terminal sliding mode controller. Int J Renew Energy Res (IJRER) 6(4):1435–1445

Bahgat ABG et al (2005) Maximum power point traking controller for PV systems using neural networks. Renew Energy 30(8):1257–1268. https://doi.org/10.1016/j.renene.2004.09.011CrossRef

Bendib B et al (2014) Advanced fuzzy MPPT controller for a stand-alone PV system. Energy Procedia 50:383–392. https://doi.org/10.1016/j.egypro.2014.06.046CrossRef

Chiu C-S (2010) T–S fuzzy maximum power point tracking control of solar power generation systems. IEEE Trans Energy Convers 25(4):1123–1132. https://doi.org/10.1109/tec.2010.2041551CrossRef

Chiu C-S, Ouyang Y-L, Ku C-Y (2012) Terminal sliding mode control for maximum power point tracking of photovoltaic power generation systems. Sol Energy 86(10):2986–2995. https://doi.org/10.1016/j.solener.2012.07.008CrossRef

Dahech K et al (2017) Backstepping sliding mode control for maximum power point tracking of a photovoltaic system. Electr Power Syst Res 143:182–188. https://doi.org/10.1016/j.epsr.2016.10.043CrossRef

Dehghanzadeh A, Farahani G, Maboodi M (2018) Maximum power point tracking of a photovoltaic system using modified incremental algorithm and model predictive control. J Control 12(2):67–75. https://doi.org/10.29252/joc.12.2.67CrossRef

Dileep G, Singh SN (2017) Selection of non-isolated DC-DC converters for solar photovoltaic system. Renew Sustain Energy Rev 76:1230–1247. https://doi.org/10.1016/j.rser.2017.03.130CrossRef

Fahmi A et al (2017) Aggregation operators on triangular cubic fuzzy numbers and its application to multi-criteria decision making problems. J Intell Fuzzy Syst 33(6):3323–3337. https://doi.org/10.3233/jifs-162007CrossRef

Fahmi A et al (2018a) Weighted average rating (war) method for solving group decision making problem using triangular cubic fuzzy hybrid aggregation (TCFHA). Punjab Univ J Math 50(1):23–34MathSciNet

Fahmi A et al (2018b) Cubic fuzzy Einstein aggregation operators and its application to decision-making. Int J Syst Sci 49(11):2385–2397. https://doi.org/10.1080/00207721.2018.1503356MathSciNetCrossRefMATH

Fahmi A et al (2018c) Trapezoidal cubic fuzzy number Einstein hybrid weighted averaging operators and its application to decision making. Soft Comput 23(14):5753–5783. https://doi.org/10.1007/s00500-018-3242-6CrossRefMATH

Fahmi A et al (2018d) Some geometric operators with triangular cubic linguistic hesitant fuzzy number and their application in group decision-making. J Intell Fuzzy Syst 35(2):2485–2499. https://doi.org/10.3233/jifs-18125CrossRef

Fahmi A et al (2018e) Triangular cubic hesitant fuzzy Einstein hybrid weighted averaging operator and its application to decision making. Symmetry. https://doi.org/10.3390/sym10110658CrossRefMATH

Fahmi A et al (2019a) Precursor selection for sol–gel synthesis of titanium carbide nanopowders by a new cubic fuzzy multi-attribute group decision-making model. J Intell Syst 28(5):699–720. https://doi.org/10.1515/jisys-2017-0083MathSciNetCrossRef

Fahmi A, Abdullah S, Amin F (2019b) Cubic uncertain linguistic powered Einstein aggregation operators and their application to multi-attribute group decision making. Math Sci 13(2):129–152. https://doi.org/10.1007/s40096-019-0285-5MathSciNetCrossRefMATH

Fahmi A et al (2019c) Group decision making based on triangular neutrosophic cubic fuzzy einstein hybrid weighted averaging operators. Symmetry. https://doi.org/10.3390/sym11020180CrossRefMATH

Fahmi A et al (2019d) Trapezoidal linguistic cubic fuzzy TOPSIS method and application in a group decision making program. J Intell Syst 29(1):1283–1300. https://doi.org/10.1515/jisys-2017-0560MathSciNetCrossRef

Fahmi A et al (2019e) Trapezoidal cubic hesitant fuzzy aggregation operators and their application in group decision-making. J Intell Fuzzy Syst 36(4):3619–3635. https://doi.org/10.3233/jifs-181703CrossRef

Ghazanfari J, Maghfoori Farsangi M (2013) Maximum power point tracking using sliding mode control for photovoltaic array. Iran J Electr Electron Eng 9(3):189–196

Gupta A, Chauhan YK, Pachauri RK (2016) A comparative investigation of maximum power point tracking methods for solar PV system. Sol Energy 136:236–253. https://doi.org/10.1016/j.solener.2016.07.001CrossRef

Joshi P, Arora S (2017) Maximum power point tracking methodologies for solar PV systems—a review. Renew Sustain Energy Rev 70:1154–1177. https://doi.org/10.1016/j.rser.2016.12.019CrossRef

Kchaou A et al (2017) Second order sliding mode-based MPPT control for photovoltaic applications. Sol Energy 155:758–769. https://doi.org/10.1016/j.solener.2017.07.007CrossRef

Mahamudul H et al (2013) Modelling of PV module with incremental conductance MPPT controlled buck-boost converter. In: 2013 2nd International conference on advances in electrical engineering (ICAEE). IEEE. https://doi.org/10.1109/ICAEE.2013.6750332

Mamarelis E, Petrone G, Spagnuolo G (2014) A two-steps algorithm improving the P&O steady state MPPT efficiency. Appl Energy 113:414–421. https://doi.org/10.1016/j.apenergy.2013.07.022CrossRef

Mojallizadeh MR, Badamchizadeh MA (2017) Second-order fuzzy sliding-mode control of photovoltaic power generation systems. Sol Energy 149:332–340. https://doi.org/10.1016/j.solener.2017.04.014CrossRef

Mojallizadeh MR et al (2016a) Designing a new robust sliding mode controller for maximum power point tracking of photovoltaic cells. Sol Energy 132:538–546. https://doi.org/10.1016/j.solener.2016.03.038CrossRef

Mojallizadeh MR et al (2016b) Chattering free full-order terminal sliding-mode control for maximum power point tracking of photovoltaic cells. IET Renew Power Gener 11(1):85–91. https://doi.org/10.1049/iet-rpg.2016.0188CrossRef

Pradhan R, Subudhi B (2016) Double integral sliding mode MPPT control of a photovoltaic system. IEEE Trans Control Syst Technol 24(1):285–292. https://doi.org/10.1109/tcst.2015.2420674CrossRef

Radhia G et al (2013) MPPT controller for a photovoltaic power system based on increment conductance approach. In: 2013 International conference on renewable energy research and applications (ICRERA). IEEE. https://doi.org/10.1109/ICRERA.2013.6749729

Ram JP, Babu TS, Rajasekar N (2017) A comprehensive review on solar PV maximum power point tracking techniques. Renew Sustain Energy Rev 67:826–847. https://doi.org/10.1016/j.rser.2016.09.076CrossRef

Salhi M, El-Bachtiri R (2009) Maximum power point tracking controller for PV systems using a PI regulator with boost DC/DC converter. Int Cong Glob Sci Technol. https://doi.org/10.1016/j.renene.2004.09.011CrossRef

Selmi T et al (2014) P&O mppt implementation using matlab/simulink. In: 2014 Ninth international conference on ecological vehicles and renewable energies (EVER). IEEE. https://doi.org/10.1109/ever.2014.6844065

Singh S (2017) Selection of non-isolated DC–DC converters for solar photovoltaic system. Renew Sustain Energy Rev 76:1230–1247CrossRef

Slotine J-JE, Li W (1991) Applied nonlinear control, vol 199. Prentice Hall, Englewood CliffsMATH

Wang L-X (1999) A course in fuzzy systems. Prentice-Hall Press, Hoboken

Wang J, Rad AB, Chan P (2001) Indirect adaptive fuzzy sliding mode control: Part I: fuzzy switching. Fuzzy Sets Syst 122(1):21–30. https://doi.org/10.1016/s0165-0114(99)00179-7CrossRefMATH

Zadeh LA (1965) Information and control. Fuzzy Sets 8(3):338–353. https://doi.org/10.1142/9789814261302_0021CrossRef

Titel: Maximum power point tracking in photovoltaic systems using indirect adaptive fuzzy robust controller
verfasst von: Hadi Delavari
Morteza Zolfi
Publikationsdatum: 15.05.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 16/2021
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-021-05823-0

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 "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 16/2021

Real-time sign language framework based on wearable device: analysis of MSL, DataGlove, and gesture recognition

Computational analysis on the different core configurations for metal sandwich panel under high velocity impact

An adaptive fuzzy inference approach for color image steganography

Enhancing the precision and accuracy of renal failure diagnosis using the modified support vector machine algorithm and dragonfly algorithm

Fuzzy logic approach in energy security decision-making: “ESecFuzzy” software application

Surgical rescheduling problem with emergency patients considering participants’ dissatisfaction

Premium Partner