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Combined ANFIS–Wavelet Technique to Improve the Estimation Accuracy of the Power Output of Neighboring PV Systems during Cloud Events

Author

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  • Hasanain A. H. Al-Hilfi

    (School of Electrical Engineering and Computing, Curtin University, Perth 6102, Australia
    Computer Center, Basrah University, Basrah 61028, Iraq)

  • Ahmed Abu-Siada

    (School of Electrical Engineering and Computing, Curtin University, Perth 6102, Australia)

  • Farhad Shahnia

    (School of Engineering and Information Technology, Murdoch University, Murdoch 6150, Australia)

Abstract

The short-term variability of photovoltaic (PV) system-generated power due to ambient conditions, such as passing clouds, represents a key challenge for network planners and operators. Such variability can be reduced using a geographical smoothing technique based on installing multiple PV systems over certain locations at distances of meters to kilometers. To accurately estimate the PV system’s generated power during cloud events, a variability reduction index ( VRI ), which is a function of several parameters, should be calculated precisely. In this paper, the Wavelet Transform Technique ( WTT ) along with Adaptive Neuro Fuzzy Inference System (ANFIS) are used to develop new models to estimate the PV system’s power output during cloud events. In this context, irradiance data collected from one PV system along with other parameters, including ambient conditions, were used to develop the proposed models. Ultimately, the models were validated through their application on a 0.7 km 2 PV plant with 16 rooftop PV systems in Brisbane, Australia.

Suggested Citation

  • Hasanain A. H. Al-Hilfi & Ahmed Abu-Siada & Farhad Shahnia, 2020. "Combined ANFIS–Wavelet Technique to Improve the Estimation Accuracy of the Power Output of Neighboring PV Systems during Cloud Events," Energies, MDPI, vol. 13(7), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1613-:d:340190
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    References listed on IDEAS

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    1. Tianyang Wang & James S. Dyer & Warren J. Hahn, 2017. "Sensitivity analysis of decision making under dependent uncertainties using copulas," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 117-139, November.
    2. Tang, Yuchen & Cheng, John W.M. & Duan, Qinwei & Lee, Cheuk Wing & Zhong, Jin, 2019. "Evaluating the variability of photovoltaics: A new stochastic method to generate site-specific synthetic solar data and applications to system studies," Renewable Energy, Elsevier, vol. 133(C), pages 1099-1107.
    3. Rowlands, Ian H. & Kemery, Briana Paige & Beausoleil-Morrison, Ian, 2014. "Managing solar-PV variability with geographical dispersion: An Ontario (Canada) case-study," Renewable Energy, Elsevier, vol. 68(C), pages 171-180.
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    Cited by:

    1. Chao-Rong Chen & Faouzi Brice Ouedraogo & Yu-Ming Chang & Devita Ayu Larasati & Shih-Wei Tan, 2021. "Hour-Ahead Photovoltaic Output Forecasting Using Wavelet-ANFIS," Mathematics, MDPI, vol. 9(19), pages 1-14, October.

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