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Novel Improved Adaptive Neuro-Fuzzy Control of Inverter and Supervisory Energy Management System of a Microgrid

Author

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  • Tariq Kamal

    (Department of Electrical and Electronics Engineering, Sakarya University, Faculty of Engineering, 54050 Serdivan/Sakarya, Turkey
    Research Group in Sustainable and Renewable Electrical Technologies (PAIDI-TEP-023), University of Cadiz, Higher Polytechnic School of Algeciras, 11202 Algeciras (Cadiz), Spain)

  • Murat Karabacak

    (Department of Electrical and Electronics Engineering, Sakarya University of Applied Sciences, 54050 Serdivan/Sakarya, Turkey)

  • Vedran S. Perić

    (Munich School of Engineering, Technical University of Munich, 85748 Garching, Germany)

  • Syed Zulqadar Hassan

    (Department of Electrical Engineering, Faculty of Engineering & Architecture, University of Sialkot, 51040 Sialkot, Pakistan)

  • Luis M. Fernández-Ramírez

    (Research Group in Sustainable and Renewable Electrical Technologies (PAIDI-TEP-023), University of Cadiz, Higher Polytechnic School of Algeciras, 11202 Algeciras (Cadiz), Spain)

Abstract

In this paper, energy management and control of a microgrid is developed through supervisor and adaptive neuro-fuzzy wavelet-based control controllers considering real weather patterns and load variations. The supervisory control is applied to the entire microgrid using lower–top level arrangements. The top-level generates the control signals considering the weather data patterns and load conditions, while the lower level controls the energy sources and power converters. The adaptive neuro-fuzzy wavelet-based controller is applied to the inverter. The new proposed wavelet-based controller improves the operation of the proposed microgrid as a result of the excellent localized characteristics of the wavelets. Simulations and comparison with other existing intelligent controllers, such as neuro-fuzzy controllers and fuzzy logic controllers, and classical PID controllers are used to present the improvements of the microgrid in terms of the power transfer, inverter output efficiency, load voltage frequency, and dynamic response.

Suggested Citation

  • Tariq Kamal & Murat Karabacak & Vedran S. Perić & Syed Zulqadar Hassan & Luis M. Fernández-Ramírez, 2020. "Novel Improved Adaptive Neuro-Fuzzy Control of Inverter and Supervisory Energy Management System of a Microgrid," Energies, MDPI, vol. 13(18), pages 1-22, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4721-:d:411731
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    References listed on IDEAS

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    1. S. Ananda Kumar & M. S. P. Subathra & Nallapaneni Manoj Kumar & Maria Malvoni & N. J. Sairamya & S. Thomas George & Easter S. Suviseshamuthu & Shauhrat S. Chopra, 2020. "A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network," Energies, MDPI, vol. 13(16), pages 1-22, August.
    2. Syed Zulqadar Hassan & Hui Li & Tariq Kamal & Uğur Arifoğlu & Sidra Mumtaz & Laiq Khan, 2017. "Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 10(3), pages 1-16, March.
    3. Maria Grazia De Giorgi & Stefano Campilongo & Antonio Ficarella & Paolo Maria Congedo, 2014. "Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)," Energies, MDPI, vol. 7(8), pages 1-22, August.
    4. Cristian Verdugo & Samir Kouro & Christian A. Rojas & Marcelo A. Perez & Thierry Meynard & Mariusz Malinowski, 2019. "Five-Level T-type Cascade Converter for Rooftop Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 12(9), pages 1-20, May.
    5. Stéfano Frizzo Stefenon & Roberto Zanetti Freire & Leandro dos Santos Coelho & Luiz Henrique Meyer & Rafael Bartnik Grebogi & William Gouvêa Buratto & Ademir Nied, 2020. "Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System," Energies, MDPI, vol. 13(2), pages 1-19, January.
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    Cited by:

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