  The increase in the use of photovoltaic (PV) energy in the world has shown
that the useful life and maintenance of a PV plant directly depend on
theability to quickly detect severe faults on a PV plant. To solve this problem
of detection, data based approaches have been proposed in the
literature.However, these previous solutions consider only specific behavior of
one or few faults. Most of these approaches can be qualified as supervised,
requiring an enormous labelling effort (fault types clearly identified in each
technology). In addition, most of them are validated in PV cells or one PV
module. That is hardly applicable in large-scale PV plants considering their
complexity. Alternatively, some unsupervised well-known approaches based on
data try to detect anomalies but are not able to identify precisely the type of
fault. The most performant of these methods do manage to efficiently group
healthy panels and separate them from faulty panels. In that way, this article
presents an unsupervised approach called DTW K-means. This approach takes
advantages of both the dynamic time warping (DWT) metric and the Kmeans
clustering algorithm as a data-driven approach. The results of this mixed
method in a PV string are compared to diagnostic labels established by visual
inspection of the panels.
