Solar energy converter devices such as photovoltaic cells are very sensitive to instantaneous solar radiation fluctuations. Thus rapid variation of solar radiation due to changes in the local meteorological condition can induce large amplitude fluctuations of the produced electrical power and reduce the over-all efficiency of the system. When large amount of photovoltaic electricity is send into a weak or small electricity network such as island network; the electric grid security can be in jeopardy due to these power fluctuations. To palliate these difficulties, it is essential to identify the characteristic of these fluctuations in order to anticipate the eventuality of power shortage or power surge. The detection of singularity in solar radiation signal allows separating the signal in different sequences, as a function of the different meteorological conditions encountered at the measurement site. Thereby, the different sequences could be analyzed separately. For instance, a statistical analysis could be conducted in order to classify these sequences as a function of their effect on the overall efficiency of a photovoltaic system. The objective of this paper is to present an approach that uses wavelet transform analysis to detect singularities in solar radiation signal. This preliminary work set the basis for further investigation dedicated to classification of solar energy fluctuation.
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