English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The increase in the use of photovoltaic (PV) energy in the world has shownthat the useful life and maintenance of a PV plant directly depend ontheability to quickly detect severe faults on a PV plant. To solve this problemof detection, data based approaches have been proposed in theliterature.However, these previous solutions consider only specific behavior ofone or few faults. Most of these approaches can be qualified as supervised,requiring an enormous labelling effort (fault types clearly identified in eachtechnology). In addition, most of them are validated in PV cells or one PVmodule. That is hardly applicable in large-scale PV plants considering theircomplexity. Alternatively, some unsupervised well-known approaches based ondata try to detect anomalies but are not able to identify precisely the type offault. The most performant of these methods do manage to efficiently grouphealthy panels and separate them from faulty panels. In that way, this articlepresents an unsupervised approach called DTW K-means. This approach takesadvantages of both the dynamic time warping (DWT) metric and the Kmeansclustering algorithm as a data-driven approach. The results of this mixedmethod in a PV string are compared to diagnostic labels established by visualinspection of the panels.

DTW k-means clustering for fault detection in photovoltaic modules