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

Hasanain A. H. Al-Hilfi, Ahmed Abu-Siada and Farhad Shahnia
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Hasanain A. H. Al-Hilfi: School of Electrical Engineering and Computing, Curtin University, Perth 6102, Australia
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

Energies, 2020, vol. 13, issue 7, 1-15

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.

Keywords: photovoltaic system; geographic smoothing; variability reduction index; ANFIS; wavelet transform (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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