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Experimental Assessment of a Novel Irradiance Sensorless Intelligent Control Scheme for a Standalone Photovoltaic System under Real Climatic Conditions

Author

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  • Jialan Sun

    (College of Mechanical & Energy Engineering, Beijing University of Technology, Beijing 100124, China
    CCTEG China Coal Research Institute, Beijing 100013, China
    State Key Laboratory of Intelligent Coal Mining and Strata Control, Beijing 100013, China
    Engineering Research Center for Technology Equipment of Emergency Refuge in Coal Mine, Beijing 100013, China)

  • Jinwei Fan

    (College of Mechanical & Energy Engineering, Beijing University of Technology, Beijing 100124, China)

Abstract

The efficiency of standalone photovoltaic (PV) systems heavily relies on the effectiveness of their maximum power point tracking (MPPT) controller. This study aims to improve the operational efficiency and reliability of standalone PV systems by introducing a novel control scheme, the Immersion and Invariance Neural Network (II-NN). This innovative system integrates a nonlinear estimator of solar irradiance with a neural network (NN) model, eliminating the need for direct irradiance measurements and associated costly sensors. The proposed methodology uses the Immersion and Invariance algorithm to design a nonlinear estimator that leverages the real-time measurements of PV current and voltage to estimate the incident irradiance. The NN then processes this estimated irradiance to determine the MPP voltage accurately. A robust nonlinear controller ensures the PV system operates at the MPP. This approach stands out by managing the nonlinearities, parametric uncertainties, and dynamic variations in PV systems without relying on direct irradiance measurements. The II-NN system was rigorously tested and validated under real climatic conditions, providing a realistic performance assessment. The principal results show that the II-NN system achieves a mean error of 0.0183V and a mean absolute percentage error of 0.3913%, with an overall MPPT efficiency of up to 99.84%. Comparisons with the existing methods, including perturb and observe, incremental conductance, and three other recent algorithms, reveal that the II-NN system outperforms these alternatives. The major conclusion is that the II-NN algorithm significantly enhances the operational efficiency of PV systems while simplifying their implementation, making them more cost-effective and accessible. This study substantially contributes to PV system control by advancing a robust, intelligent, and sensorless MPPT control scheme that maintains high performance even under varying and unpredictable climatic conditions.

Suggested Citation

  • Jialan Sun & Jinwei Fan, 2024. "Experimental Assessment of a Novel Irradiance Sensorless Intelligent Control Scheme for a Standalone Photovoltaic System under Real Climatic Conditions," Energies, MDPI, vol. 17(18), pages 1-31, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:18:p:4627-:d:1478784
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    References listed on IDEAS

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    1. Marzband, Mousa & Sumper, Andreas & Ruiz-Álvarez, Albert & Domínguez-García, José Luis & Tomoiagă, Bogdan, 2013. "Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets," Applied Energy, Elsevier, vol. 106(C), pages 365-376.
    2. Qais, Mohammed H. & Hasanien, Hany M. & Alghuwainem, Saad, 2019. "Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm," Applied Energy, Elsevier, vol. 250(C), pages 109-117.
    3. Slimane Hadji & Jean-Paul Gaubert & Fateh Krim, 2018. "Real-Time Genetic Algorithms-Based MPPT: Study and Comparison (Theoretical an Experimental) with Conventional Methods," Energies, MDPI, vol. 11(2), pages 1-17, February.
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