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 km2 PV plant with 16 rooftop PV systems in Brisbane, Australia.
Highlights
Global warming due to greenhouse gas emissions produced by conventional fossil fuel-based power plants has urged all nations to invest in renewable energy, such as photovoltaic (PV) systems and wind energy [1]
Because variability is the main concern by power operators and planners, the variability power index (Vpi ) has been introduced as a mathematical tool to show the amount of the power fluctuation at each Discrete Wavelet Transform (DWT) mode
Was analyzed from six that wavelet modesmodel using proposed in this paper attains a minimum and maximum improvement of Equation (8), and the wavelet periodograms factor in Equation (15) was employed at each wavelet compared with the variability reduction index (VRI)–Gene Expression Programming (GEP)
Summary
Global warming due to greenhouse gas emissions produced by conventional fossil fuel-based power plants has urged all nations to invest in renewable energy, such as photovoltaic (PV) systems and wind energy [1]. Short-term irradiance fluctuation due to the unexpected passing of clouds of unknown size, direction and speed, may result in frequency instability due to the reduction in the generated power [4,5,6] Estimating such power is one of the main challenges faced by network operators to prevent frequency instability due to such short-term power reduction, which is why many adopt a suitable backup energy storage system. Few studies have been introduced to estimate the power output the Wavelet Transform Technique based on measured data of only one PV sensor and by considering variability using the Wavelet Transform Technique based on measured data of only one PV sensor the PV system’s locations, cloud speed and irradiance time series [21,22,23]. A combined WTT and Adaptive Neuro Fuzzy Inference System (ANFIS)-based technique with a higher estimation accuracy than existing models are presented.
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