Abstract
Fluctuations in solar irradiance are a serious obstacle for the future large-scale application of photovoltaics. Occurring regularly with the passage of clouds, they can cause unexpected power variations and introduce voltage dips to the power distribution system. This paper proposes the treatment of such fluctuating time series as realizations of a stochastic, locally stationary, wavelet process. Its local spectral density can be estimated from empirical data by means of wavelet periodograms. The wavelet approach allows the analysis of the amplitude of fluctuations per characteristic scale, hence, persistence of the fluctuation. Furthermore, conclusions can be drawn on the frequency of occurrence of fluctuations of dierent scale. This localized spectral analysis was applied to empirical data of two successive years. The approach is especially useful for network planning and load management of power distribution systems containing a high density of photovoltaic generation units.
Highlights
Most applications of wavelet decomposition in the field of electrical power engineering concern the analysis of load profiles [1, 2], the electrical power supply quality and its measurement [3,4,5,6], and protection issues [7]
The approach is especially useful for network planning and load management of power distribution systems containing a high density of photovoltaic generation units
A new area of application is presented with the analysis of time series of solar radiation in order to quantify the intermittent power supplied by solar energy systems, mainly photovoltaics (PV)
Summary
Most applications of wavelet decomposition in the field of electrical power engineering concern the analysis of load profiles [1, 2], the electrical power supply quality and its measurement [3,4,5,6], and protection issues [7]. A new area of application is presented with the analysis of time series of solar radiation in order to quantify the intermittent power supplied by solar energy systems, mainly photovoltaics (PV). In this case, the power supply quality can be deteriorated as a consequence of power variations due to a varying cloud coverage of the sky. The clearness index k accounts for all meteorological influences, mainly being the stochastic parameters atmospheric turbidity and moving clouds. It is independent of all astronomical relationships. The sampling period ΔT for an analysis of cloud-induced fluctuations should be no longer than eight seconds in order to account for more than 98% of the signal’s power content [10]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
