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A Novel Intelligent ANFIS for the Dynamic Model of Photovoltaic Systems

Author

Listed:
  • Abdelhady Ramadan

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Salah Kamel

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • I. Hamdan

    (Department of Electrical Engineering, Faculty of Engineering, South Valley University, Qena 83523, Egypt)

  • Ahmed M. Agwa

    (Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 1321, Saudi Arabia
    Prince Faisal bin Khalid bin Sultan Research Chair in Renewable Energy Studies and Applications (PFCRE), Northern Border University, Arar 1321, Saudi Arabia)

Abstract

Developing accurate models for photovoltaic (PV) systems has a significant impact on the evaluation of the accuracy and testing of PV systems. Artificial intelligence (AI) is the science of developing machine jobs to be more intelligent, similar to the human brain. Involving AI techniques in modeling has a significant modification in the accuracy of the developed models. In this paper, a novel dynamic PV model based on AI is proposed. The proposed dynamic PV model was designed based on an adaptive neuro-fuzzy inference system (ANFIS). ANFIS is a combination of a neural network and a fuzzy system; thus, it has the advantages of both techniques. The design process is well discussed. Several types of membership functions, different numbers of training, and different numbers of membership functions are tested via MATLAB simulations until the AI requirements of the ANFIS model are satisfied. The obtained model is evaluated by comparing the model accuracy with the classical dynamic models proposed in the literature. The root mean square error (RMSE) of the real PV system output current is compared with the output current of the proposed PV model. The ANFIS model is trained based on input–output data captured from a real PV system under specified irradiance and temperature conditions. The proposed model is compared with classical dynamic PV models such as the integral-order model (IOM) and fractional-order model (FOM), which have been proposed in the literature. The use of ANFIS to model dynamic PV systems achieves an accurate dynamic PV model in comparison with the classical dynamic IOM and FOM.

Suggested Citation

  • Abdelhady Ramadan & Salah Kamel & I. Hamdan & Ahmed M. Agwa, 2022. "A Novel Intelligent ANFIS for the Dynamic Model of Photovoltaic Systems," Mathematics, MDPI, vol. 10(8), pages 1-14, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1286-:d:792363
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    References listed on IDEAS

    as
    1. Abdelhady Ramadan & Salah Kamel & Mohamed H. Hassan & Marcos Tostado-Véliz & Ali M. Eltamaly, 2021. "Parameter Estimation of Static/Dynamic Photovoltaic Models Using a Developed Version of Eagle Strategy Gradient-Based Optimizer," Sustainability, MDPI, vol. 13(23), pages 1-29, November.
    2. Prasert Aengchuan & Busaba Phruksaphanrat, 2018. "Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS + ANN) and FIS with adaptive neuro-fuzzy inference system (FIS + ANFIS) for inventory control," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 905-923, April.
    3. Abd-ElHady Ramadan & Salah Kamel & Tahir Khurshaid & Seung-Ryle Oh & Sang-Bong Rhee, 2021. "Parameter Extraction of Three Diode Solar Photovoltaic Model Using Improved Grey Wolf Optimizer," Sustainability, MDPI, vol. 13(12), pages 1-16, June.
    4. Rui Castro & Miguel Silva, 2021. "Experimental and Theoretical Validation of One Diode and Three Parameters–Based PV Models," Energies, MDPI, vol. 14(8), pages 1-25, April.
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    Cited by:

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    2. Elham Alzain & Shaha Al-Otaibi & Theyazn H. H. Aldhyani & Ali Saleh Alshebami & Mohammed Amin Almaiah & Mukti E. Jadhav, 2023. "Revolutionizing Solar Power Production with Artificial Intelligence: A Sustainable Predictive Model," Sustainability, MDPI, vol. 15(10), pages 1-21, May.

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    Keywords

    AI; PV; ANFIS; dynamic IOM; dynamic FOM;
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