Research article

A novel mine blast optimization algorithm (MBOA) based MPPT controlling for grid-PV systems

  • Received: 01 February 2023 Revised: 28 March 2023 Accepted: 30 March 2023 Published: 20 April 2023
  • One of the most important areas in today's world is meeting the energy needs of various resources provided by nature. The advantages of renewable energy sources for many application sectors have attracted a lot of attention. The majority of grid-based enterprises use solar photovoltaic (PV) systems to collect sunlight as a reliable energy source. Due to solar PV's simple accessibility and efficient panel design, it is widely used in a variety of application scenarios. By employing the Maximum Power Point Tracking (MPPT) technique, the PV modules can typically operate at their best rate and draw the most power possible from the solar system. Some hybrid control mechanisms are utilized in solar PV systems in traditional works, which has limitations on the problems of increased time consumption, decreased efficiency, and increased THD. Thus, a new Mine Blast Optimization Algorithm (MBOA) based MPPT controlling model is developed to maximize the electrical energy produced by the PV panels under a different climatic situations. Also, an interleaved Luo DC-DC converter is used to significantly improve the output voltage of a PV system with a lower switching frequency. A sophisticated converter and regulating models are being created to effectively meet the energy demand of grid systems. The voltage source inverter is used to lower the level of harmonics and ensure the grid systems' power quality. Various performance indicators are applied to assess the simulation and comparative results of the proposed MBOA-MPPT controlling technique integrated with an interleaved Luo converter.

    Citation: I.E.S. Naidu, S. Srikanth, A. Siva sarapakara Rao, Adabala Venkatanarayana. A novel mine blast optimization algorithm (MBOA) based MPPT controlling for grid-PV systems[J]. AIMS Electronics and Electrical Engineering, 2023, 7(2): 135-155. doi: 10.3934/electreng.2023008

    Related Papers:

  • One of the most important areas in today's world is meeting the energy needs of various resources provided by nature. The advantages of renewable energy sources for many application sectors have attracted a lot of attention. The majority of grid-based enterprises use solar photovoltaic (PV) systems to collect sunlight as a reliable energy source. Due to solar PV's simple accessibility and efficient panel design, it is widely used in a variety of application scenarios. By employing the Maximum Power Point Tracking (MPPT) technique, the PV modules can typically operate at their best rate and draw the most power possible from the solar system. Some hybrid control mechanisms are utilized in solar PV systems in traditional works, which has limitations on the problems of increased time consumption, decreased efficiency, and increased THD. Thus, a new Mine Blast Optimization Algorithm (MBOA) based MPPT controlling model is developed to maximize the electrical energy produced by the PV panels under a different climatic situations. Also, an interleaved Luo DC-DC converter is used to significantly improve the output voltage of a PV system with a lower switching frequency. A sophisticated converter and regulating models are being created to effectively meet the energy demand of grid systems. The voltage source inverter is used to lower the level of harmonics and ensure the grid systems' power quality. Various performance indicators are applied to assess the simulation and comparative results of the proposed MBOA-MPPT controlling technique integrated with an interleaved Luo converter.



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    [1] Kumar SP, Agyekum EB, Kumar A, Velkin VI (2023) Performance evaluation with low-cost aluminum reflectors and phase change material integrated to solar PV modules using natural air convection: An experimental investigation. Energy 266: 126415. https://doi.org/10.1016/j.energy.2022.126415 doi: 10.1016/j.energy.2022.126415
    [2] Praveenkumar S, Agyekum EB, Kumar A, Velkin VI (2023) Thermo-enviro-economic analysis of solar photovoltaic/thermal system incorporated with u-shaped grid copper pipe, thermal electric generators and nanofluids: An experimental investigation. J Energy Storage 60: 106611. https://doi.org/10.1016/j.est.2023.106611 doi: 10.1016/j.est.2023.106611
    [3] Essa ME-SM, Hussian OS, Hassan MM (2021) Intelligent Fractional Control Design of MPPT for a Standalone PV System Based on Optimization Technique. 2021 17th International Computer Engineering Conference (ICENCO), 107‒111. IEEE. https://doi.org/10.1109/ICENCO49852.2021.9698966
    [4] Subramanian A, Jayaparvathy R (2021) Performance comparison of modified elephant herding optimization tuned MPPT for PV based solar energy systems. Circuit World 48: 309‒321. https://doi.org/10.1108/CW-11-2020-0316 doi: 10.1108/CW-11-2020-0316
    [5] Subramanian A, Raman J (2021) Grasshopper optimization algorithm tuned maximum power point tracking for solar photovoltaic systems. J Amb Intel Hum Comp 12: 8637‒8645. https://doi.org/10.1007/s12652-020-02593-9 doi: 10.1007/s12652-020-02593-9
    [6] Kihal A, Krim F, Laib A, Talbi B, Afghoul H (2019) An improved MPPT scheme employing adaptive integral derivative sliding mode control for photovoltaic systems under fast irradiation changes. ISA T 87: 297‒306. https://doi.org/10.1016/j.isatra.2018.11.020 doi: 10.1016/j.isatra.2018.11.020
    [7] Mirza AF, Mansoor M, Ling Q, Yin B, Javed MY (2020) A Salp-Swarm Optimization based MPPT technique for harvesting maximum energy from PV systems under partial shading conditions. Energ Convers Manage 209: 112625. https://doi.org/10.1016/j.enconman.2020.112625 doi: 10.1016/j.enconman.2020.112625
    [8] Mirza AF, Mansoor M, Ling Q, Khan MI, Aldossary OM (2020) Advanced variable step size incremental conductance MPPT for a standalone PV system utilizing a GA-tuned PID controller. Energies 13: 1‒25. https://doi.org/10.3390/en13164153 doi: 10.3390/en13164153
    [9] Karrag A, Messalti S (2019) PSO‐based SMC variable step size P & O MPPT controller for PV systems under fast changing atmospheric conditions. Int J Numer Model El 32: e2603. https://doi.org/10.1002/jnm.2603 doi: 10.1002/jnm.2603
    [10] Mahesh PV, Meyyappan S, Alla RKR (2022) A new multivariate linear regression MPPT algorithm for solar PV system with boost converter. ECTI Transactions on Electrical Engineering, Electronics, and Communications 20: 269‒281. https://doi.org/10.37936/ecti-eec.2022202.246909 doi: 10.37936/ecti-eec.2022202.246909
    [11] Ebrahim M, Osama A, Kotb KM, Bendary F (2019) Whale inspired algorithm based MPPT controllers for grid-connected solar photovoltaic system. Energy Procedia 162: 77‒86. https://doi.org/10.1016/j.egypro.2019.04.009 doi: 10.1016/j.egypro.2019.04.009
    [12] Aly M, Rezk H (2022) An improved fuzzy logic control-based MPPT method to enhance the performance of PEM fuel cell system. Neural Computing and Applications 34: 4555‒4566. https://doi.org/10.1007/s00521-021-06611-5 doi: 10.1007/s00521-021-06611-5
    [13] Chauhan U, Singh V, Kumar B, Rani A (2020) An improved MVO assisted global MPPT algorithm for partially shaded PV system. J Intell Fuzzy Syst 38: 6715‒6726. https://doi.org/10.3233/JIFS-179749 doi: 10.3233/JIFS-179749
    [14] Gupta AK, Pachauri RK, Maity T, Chauhan YK, Mahela OP, Khan B, et al. (2021) Effect of various incremental conductance MPPT methods on the charging of battery load feed by solar panel. IEEE Access 9: 90977‒90988. https://doi.org/10.1109/ACCESS.2021.3091502 doi: 10.1109/ACCESS.2021.3091502
    [15] Wasim MS, Amjad M, Habib S, Abbasi MA, Bhatti AR, Muyeen S (2022) A critical review and performance comparisons of swarm-based optimization algorithms in maximum power point tracking of photovoltaic systems under partial shading conditions. Energy Reports 8: 4871‒4898. https://doi.org/10.1016/j.egyr.2022.03.175 doi: 10.1016/j.egyr.2022.03.175
    [16] Dagal I, Akın B, Akboy E (2022) MPPT mechanism based on novel hybrid particle swarm optimization and salp swarm optimization algorithm for battery charging through simulink. Scientific reports 12: 1‒17. https://doi.org/10.1038/s41598-022-06609-6 doi: 10.1038/s41598-021-99269-x
    [17] González-Castaño C, Restrepo C, Kouro S, Rodriguez J (2021) MPPT algorithm based on artificial bee colony for PV system. IEEE Access 9: 43121‒43133. https://doi.org/10.1109/ACCESS.2021.3066281 doi: 10.1109/ACCESS.2021.3066281
    [18] Yap KY, Sarimuthu CR, Lim JM-Y (2020) Artificial intelligence based MPPT techniques for solar power system: A review. J Mod Power Syst Cle 8: 1043‒1059. https://doi.org/10.35833/MPCE.2020.000159 doi: 10.35833/MPCE.2020.000159
    [19] Mirza AF, Mansoor M, Ling Q (2020) A novel MPPT technique based on Henry gas solubility optimization. Energ Convers Manage 225: 113409. https://doi.org/10.1016/j.enconman.2020.113409 doi: 10.1016/j.enconman.2020.113409
    [20] Khan FU, Gulzar MM, Sibtain D, Usman HM, Hayat A (2020) Variable step size fractional incremental conductance for MPPT under changing atmospheric conditions. Int J Numer Model El 33: e2765. https://doi.org/10.1002/jnm.2765 doi: 10.1002/jnm.2765
    [21] Ali AIM, Mohamed HRA (2022) Improved P & O MPPT algorithm with efficient open-circuit voltage estimation for two-stage grid-integrated PV system under realistic solar radiation. Int J Elec Power 137: 107805. https://doi.org/10.1016/j.ijepes.2021.107805 doi: 10.1016/j.ijepes.2021.107805
    [22] Bahari MI, Tarassodi P, Naeini YM, Khalilabad AK, Shirazi P (2016) Modeling and simulation of hill climbing MPPT algorithm for photovoltaic application. 2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 1041‒1044. https://doi.org/10.1109/SPEEDAM.2016.7525990
    [23] Hamouda N, Babes B, Kahla S, Boutaghane A, Beddar A, Aissa O (2020) ANFIS controller design using PSO algorithm for MPPT of solar PV system powered brushless DC motor based wire feeder unit. 2020 International Conference on Electrical Engineering (ICEE), 1‒6. https://doi.org/10.1109/ICEE49691.2020.9249869
    [24] Laxman B, Annamraju A, Srikanth NV (2021) A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids. Int J Hydrogen Energy 46: 10653‒10665. https://doi.org/10.1016/j.ijhydene.2020.12.158 doi: 10.1016/j.ijhydene.2020.12.158
    [25] Mohammed SS, Devaraj D, Ahamed TI (2021) GA-optimized fuzzy-based MPPT technique for abruptly varying environmental conditions. Journal of The Institution of Engineers (India): Series B 102: 497‒508. https://doi.org/10.1007/s40031-021-00552-2 doi: 10.1007/s40031-021-00552-2
    [26] Divyasharon R, Banu RN, Devaraj D (2019) Artificial neural network based MPPT with CUK converter topology for PV systems under varying climatic conditions. 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), 1‒6. https://doi.org/10.1109/INCOS45849.2019.8951321
    [27] VanDeventer W, Jamei E, Thirunavukkarasu GS, Seyedmahmoudian M, Soon TK, Horan B, et al. (2019) Short-term PV power forecasting using hybrid GASVM technique. Renew Energy 140: 367‒379. https://doi.org/10.1016/j.renene.2019.02.087 doi: 10.1016/j.renene.2019.02.087
    [28] Sobri S, Koohi-Kamali S, Rahim NA (2018) Solar photovoltaic generation forecasting methods: A review. Energ Convers Manage 156: 459‒497. https://doi.org/10.1016/j.enconman.2017.11.019 doi: 10.1016/j.enconman.2017.11.019
    [29] Chang JF, Dong N, Ip WH, Yung KL (2019) An ensemble learning model based on Bayesian model combination for solar energy prediction. J Renew Sustain Ener 11: 043702. https://doi.org/10.1063/1.5094534 doi: 10.1063/1.5094534
    [30] Siwakoti YP, Blaabjerg F (2017) Common-ground-type transformerless inverters for single-phase solar photovoltaic systems. IEEE T Ind Electron 65: 2100‒2111. https://doi.org/10.1109/TIE.2017.2740821 doi: 10.1109/TIE.2017.2740821
    [31] Beena V, Jayaraju M, Davis S (2018) Active and reactive power control of single phase transformerless grid connected inverter for distributed generation system. Int J Appl Eng Res 13: 150‒157.
    [32] Yadeo D, Chaturvedi P, Suryawanshi HM, Atkar D, Saketi SK (2021) Transistor clamped dual active bridge DC‐DC converter to reduce voltage and current stress in low voltage distribution network. Int T Electr Energy 31: e12665. https://doi.org/10.1002/2050-7038.12665 doi: 10.1002/2050-7038.12665
    [33] Lakshmi M, Hemamalini S (2019) Coordinated control of MPPT and voltage regulation using single-stage high gain DC–DC converter in a grid-connected PV system. Electr Pow Syst Res 169: 65‒73. https://doi.org/10.1016/j.epsr.2018.12.011 doi: 10.1016/j.epsr.2018.12.011
    [34] Prasad V, Jayasree P, Sruthy V (2018) Active power sharing and reactive power compensation in a grid-tied photovoltaic system. Materials Today: Proceedings 5: 1537‒1544. https://doi.org/10.1016/j.matpr.2017.11.243 doi: 10.1016/j.matpr.2017.11.243
    [35] Somalinga SS, Santha K (2021) Modified high-efficiency bidirectional DC–DC converter topology. J Power Electron 21: 257‒268. https://doi.org/10.1007/s43236-020-00160-1 doi: 10.1007/s43236-020-00160-1
    [36] Rahmani B, Li W, Liu G (2015) An Advanced Universal Power Quality Conditioning System and MPPT method for grid integration of photovoltaic systems. Int J Elec Power 69: 76‒84. https://doi.org/10.1016/j.ijepes.2014.12.031 doi: 10.1016/j.ijepes.2014.12.031
    [37] Yang B, Yu T, Shu H, Zhu D, An N, Sang Y, et al. (2018) Perturbation observer based fractional-order sliding-mode controller for MPPT of grid-connected PV inverters: Design and real-time implementation. Control Eng Pract 79: 105‒125. https://doi.org/10.1016/j.conengprac.2018.07.007 doi: 10.1016/j.conengprac.2018.07.007
    [38] Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2012) Mine blast algorithm for optimization of truss structures with discrete variables. Comput Struct 102: 49‒63. https://doi.org/10.1016/j.compstruc.2012.03.013 doi: 10.1016/j.compstruc.2012.03.013
    [39] Yıldız BS (2020) The mine blast algorithm for the structural optimization of electrical vehicle components. Mater Test 62: 497‒502. https://doi.org/10.3139/120.111511 doi: 10.3139/120.111511
    [40] Jothimani G, Palanichamy Y, Natarajan SK, Rameshkumar T (2021) Single‐phase front‐end modified interleaved Luo power factor correction converter for on‐board electric vehicle charger. Int J Circ Theor App 49: 2655‒2669. https://doi.org/10.1002/cta.3017 doi: 10.1002/cta.3017
    [41] Singh B, Kushwaha R (2021) Power factor preregulation in interleaved Luo converter-fed electric vehicle battery charger. IEEE T Ind Appl 57: 2870‒2882. https://doi.org/10.1109/TIA.2021.3061964 doi: 10.1109/TIA.2021.3061964
    [42] Chauhan U, Rani A, Kumar B, Singh V (2019) A multi verse optimization based MPPT controller for drift avoidance in solar system. J Intell Fuzzy Syst 36: 2175‒2184. https://doi.org/10.3233/JIFS-169929 doi: 10.3233/JIFS-169929
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