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Adaptive LMS MPPT Controller and Adaptive Inverter Control Law to Control the Solar Photovoltaic System

Received: 17 August 2022    Accepted: 16 September 2022    Published: 5 June 2023
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Abstract

The objective of the proposed work is to develop the maximum power point tracking controller and inverter controller by applying the adaptive Least mean square algorithm to control the total harmonics distortion of a solar photovoltaic system. The advantage of the adaptive LMS algorithm is simple and required less computational time. The adaptive LMS algorithm is applied to modify the perturbation and observation, maximum power point tracking controller. In this controller, the adaptive LMS algorithm is used to predict solar photovoltaic power. The development of the inverter control law is done using the d-q frame theory. This helps to reduce the number of equations to build a control law. The load current, grid current and grid voltage are sensed and transformed into d and q components. This adaptive LMS control law is used to extract the reference grid currents and later compared them to the actual grid currents. The comparison result is used to generate the switching gate pulses for inverter switches. The proposed controllers are developed and implemented with a solar PV system in MATLAB Simulink. The total harmonics distortion in current and voltage is investigated under linear and non-linear load conditions with changes in solar irradiations. The analysis is done by selecting step incremental values and sampling time.

Published in American Journal of Electrical Power and Energy Systems (Volume 12, Issue 2)
DOI 10.11648/j.epes.20231202.12
Page(s) 32-39
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Solar PV System, MPPT Controller, Inverter Controller, Adaptive Control Algorithm, Power Quality Issues

References
[1] Iweh, C. D.; Gyamfi, S.; Tanyi, E.; Effah-Donyina, E. (2021). Distributed Generation and Renewable Energy Integration into the Grid: Prerequisites, Push Factors, Practical Options, Issues and Merits. Energies, 14, 5375. https://doi.org/10.3390/en141753
[2] Math H. J. Bollen, Fainan Hassan. (2018). Integration of Distributed Generation in the Power System. Wiley. reprint, ISBN: 978-81-265-7326-4.
[3] Harsh Patel, Rital Gajjar, Rajen Pandya, ‘Artificial Intelligence Based MPPT techniques for Solar V System: A Review, Journal of Emerging Technologies and Innovative Research, 2019, ISSN-2349-5162.
[4] Pallavi Verma, Priya Mahajan et. al (2020). Smooth LMS-based adaptive control of SV system tied to the grid for enhanced power quality. IET Power Electronics September 2020.
[5] Obaidullah Lodin, Inderpreet Kaur, Harpreet Kaur. (2019) Predictive- P& O MPPT Algorithm for Fast and Reliable Tracking of Maximum Power Point in Solar Energy Systems. Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, Volume-7, Issue-6S4, April 2019.
[6] Satish Choudhury and Byomakesh dash et al. (2020) Comparative Analysis of LMS Based Control Algorithms for Grid Integrated System Innovation. Electrical Power Engineering, Communication and Computing Technology Springer Nature, Singapore Pte Ltd. 2020, Lecture Notes in Electrical Engineering 630, doi.org/10.1007/978-981-15-2305-2_46.
[7] Sunaina Singh and Seema et al.. (2020). Adaptive based Leaky LMS Control Technique of Grid Connected SPV System. IEEE International Conference on Power Electronics, Drives and Energy Systems 978-1-7281-5672-9.
[8] Avdhesh Kumar and Rachana Garg et al. (2021). Control of Grid Integrated Photovoltaic System using new Variable Step size Least Mean Square adaptive filter. Springer Nature, March 2021.
[9] Manoj Badoni, Alka, Ankit Kumar Singh et al. (2020). Grid Tied Solar PV system with Power Quality Enhancement Using Adaptive Generalized Maximum Versoria Criterion. DOI: 10.17775/CSEEEJPES.2020.04820.
[10] Naki Guler and Erdal Irmak (2019). MPPT Based Model Predictive Control of Grid-Connected Inverter for PV Systems. International Conference Renewable Energy Research and Applications ICERER Brasov Romana 978-1-7281-3587-8/19.
[11] Eugene Walach and B. Widrow (1984). The least mean fourth LMF adaptive algorithm and its family. IEEE Transactions on Informative Theory.
[12] M Kalai Arasi and Dr. S Shivananaitha (2018). Harmonic Reduction by LMF Algorithm in Grid-Connected SPV System. 4th International Conference on Energy Efficient Technologies for Sustainability ICEETS18.
[13] Sachin Devassy and Bhim Singh. Design and Performance Analysis of Three-Phase Solar PV Integrated UPQC. 978-1-5090-0128-6/16-IEEE.
[14] Neha Beniwal and Ikhlaq et al. (2018). Implementation of DSTATCOM with i-PNLMS Based Control Algorithm under Abnormal Grid Conditions. IEEE Transactions on Industry Applications DOI: 10.1.1094/TIA.2018.
[15] Gunjan Varshney and Madhukar Dave et al. (2019). Unit Template-based Control of PV DSTATCOM. Recent Advances in Electrical and Electronics Engineering (Formerly Recent Patents on Electrical & Electronics Engineering) 12 1-7 DOI: 10.2174/235209651166518108112853.
[16] Rahul Kumar Agarwal and Ikhlaq Hussain et al. (2017) Application of LMS-Based NN Structure for Power Quality Enhancement in a Distribution Network Under Abnormal Conditions. IEEE Transactions on Neural Networks and Learning Systems DOI; 10.1109/TNNLS.2017.2677961.
Cite This Article
  • APA Style

    Nalini Karchi, Deepak Kulkarni. (2023). Adaptive LMS MPPT Controller and Adaptive Inverter Control Law to Control the Solar Photovoltaic System. American Journal of Electrical Power and Energy Systems, 12(2), 32-39. https://doi.org/10.11648/j.epes.20231202.12

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    ACS Style

    Nalini Karchi; Deepak Kulkarni. Adaptive LMS MPPT Controller and Adaptive Inverter Control Law to Control the Solar Photovoltaic System. Am. J. Electr. Power Energy Syst. 2023, 12(2), 32-39. doi: 10.11648/j.epes.20231202.12

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    AMA Style

    Nalini Karchi, Deepak Kulkarni. Adaptive LMS MPPT Controller and Adaptive Inverter Control Law to Control the Solar Photovoltaic System. Am J Electr Power Energy Syst. 2023;12(2):32-39. doi: 10.11648/j.epes.20231202.12

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  • @article{10.11648/j.epes.20231202.12,
      author = {Nalini Karchi and Deepak Kulkarni},
      title = {Adaptive LMS MPPT Controller and Adaptive Inverter Control Law to Control the Solar Photovoltaic System},
      journal = {American Journal of Electrical Power and Energy Systems},
      volume = {12},
      number = {2},
      pages = {32-39},
      doi = {10.11648/j.epes.20231202.12},
      url = {https://doi.org/10.11648/j.epes.20231202.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20231202.12},
      abstract = {The objective of the proposed work is to develop the maximum power point tracking controller and inverter controller by applying the adaptive Least mean square algorithm to control the total harmonics distortion of a solar photovoltaic system. The advantage of the adaptive LMS algorithm is simple and required less computational time. The adaptive LMS algorithm is applied to modify the perturbation and observation, maximum power point tracking controller. In this controller, the adaptive LMS algorithm is used to predict solar photovoltaic power. The development of the inverter control law is done using the d-q frame theory. This helps to reduce the number of equations to build a control law. The load current, grid current and grid voltage are sensed and transformed into d and q components. This adaptive LMS control law is used to extract the reference grid currents and later compared them to the actual grid currents. The comparison result is used to generate the switching gate pulses for inverter switches. The proposed controllers are developed and implemented with a solar PV system in MATLAB Simulink. The total harmonics distortion in current and voltage is investigated under linear and non-linear load conditions with changes in solar irradiations. The analysis is done by selecting step incremental values and sampling time.},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Adaptive LMS MPPT Controller and Adaptive Inverter Control Law to Control the Solar Photovoltaic System
    AU  - Nalini Karchi
    AU  - Deepak Kulkarni
    Y1  - 2023/06/05
    PY  - 2023
    N1  - https://doi.org/10.11648/j.epes.20231202.12
    DO  - 10.11648/j.epes.20231202.12
    T2  - American Journal of Electrical Power and Energy Systems
    JF  - American Journal of Electrical Power and Energy Systems
    JO  - American Journal of Electrical Power and Energy Systems
    SP  - 32
    EP  - 39
    PB  - Science Publishing Group
    SN  - 2326-9200
    UR  - https://doi.org/10.11648/j.epes.20231202.12
    AB  - The objective of the proposed work is to develop the maximum power point tracking controller and inverter controller by applying the adaptive Least mean square algorithm to control the total harmonics distortion of a solar photovoltaic system. The advantage of the adaptive LMS algorithm is simple and required less computational time. The adaptive LMS algorithm is applied to modify the perturbation and observation, maximum power point tracking controller. In this controller, the adaptive LMS algorithm is used to predict solar photovoltaic power. The development of the inverter control law is done using the d-q frame theory. This helps to reduce the number of equations to build a control law. The load current, grid current and grid voltage are sensed and transformed into d and q components. This adaptive LMS control law is used to extract the reference grid currents and later compared them to the actual grid currents. The comparison result is used to generate the switching gate pulses for inverter switches. The proposed controllers are developed and implemented with a solar PV system in MATLAB Simulink. The total harmonics distortion in current and voltage is investigated under linear and non-linear load conditions with changes in solar irradiations. The analysis is done by selecting step incremental values and sampling time.
    VL  - 12
    IS  - 2
    ER  - 

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Author Information
  • Department of Electrical and Electronics Engineering, KLE Dr. M. S. Sheshgiri College of Engineering & Technology, Belagavi, India

  • Department of Electrical and Electronics Engineering, Gogte Institute of Technology, Belagavi, India

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