This paper analyzes the correlation between the fluctuations of the electrical power generated
by the ensemble of 70 DC/AC inverters from a 45.6 MW PV plant. The use of real electrical
power time series from a large collection of photovoltaic inverters of a same plant is an impor-
tant contribution in the context of models built upon simplified assumptions to overcome the
absence of such data.
This data set is divided into three different fluctuation categories with a clustering proce-
dure which performs correctly with the clearness index and the wavelet variances. Afterwards,
the time dependent correlation between the electrical power time series of the inverters is esti-
mated with the wavelet transform. The wavelet correlation depends on the distance between
the inverters, the wavelet time scales and the daily fluctuation level. Correlation values for time
scales below one minute are low without dependence on the daily fluctuation level. For time
scales above 20 minutes, positive high correlation values are obtained, and the decay rate with
the distance depends on the daily fluctuation level. At intermediate time scales the correlation
depends strongly on the daily fluctuation level.
The proposed methods have been implemented using free software. Source code is available
as supplementary material