Each behaviour group will be isolated by the clustering and analysed independently from the rest of the plant. This method allows groupings on large volumes of data, thousands of signals at a time step of a few minutes, where a human-spreadsheet analysis would simply be impossible.
To observe behavioural changes over time, a series of clustering series are carried out over the analysed period. Each observation sequence is represented by a point in a three-dimensional graph. Two dimensions for observations at a given moment and one dimension for time.
The colour of each point depends on the group in which it has been clustered. This visualization allows a quick and synthetic description of the different behaviours.
After distinguishing the different behaviours in the PV plant, the healthy behaviour is identified. The results are summarized in daily statistics with, among other things: sums, standard deviation, number of signals. The difference between the healthy behaviour and the remaining behaviours leads to the precise quantification and the evolution of the associated losses.
The other statistics inform us about the quality of clustering and the precision of the quantification of losses.