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

Phasor measurement units (PMUs) are being widely installed on power systems,providing a unique opportunity to enhance wide-area situational awareness. Oneessential application is the use of PMU data for real-time eventidentification. However, how to take full advantage of all PMU data in eventidentification is still an open problem. Thus, we propose a novel method thatperforms event identification by mining interaction graphs among differentPMUs. The proposed interaction graph inference method follows an entirelydata-driven manner without knowing the physical topology. Moreover, unlikeprevious works that treat interactive learning and event identification as twodifferent stages, our method learns interactions jointly with theidentification task, thereby improving the accuracy of graph learning andensuring seamless integration between the two stages. Moreover, to capturemulti-scale event patterns, a dilated inception-based method is investigated toperform feature extraction of PMU data. To test the proposed data-drivenapproach, a large real-world dataset from tens of PMU sources and thecorresponding event logs have been utilized in this work. Numerical resultsvalidate that our method has higher classification accuracy compared toprevious methods.

Learning Latent Interactions for Event classification via Graph Neural Networks and PMU Data