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

Today's evolving power system contains an increasing amount of powerelectronic interfaced energy sources and loads that require a paradigm shift inutility operations. Sub-synchronous oscillations at frequencies around 13-15Hz, for instance, have been reported by utilities due to wind farm controllerinteractions with the grid. Dynamics at such frequencies are unobservable bymost SCADA tools due to low sampling frequencies and lack of synchronization.Real-time or off-line frequency domain analysis of phasor measurement unit(PMU) data has become a valuable method to identify such phenomena, at theexpense of costly power system data and communication infrastructure. Thisarticle proposes an alternative machine learning (ML) based application forsub-synchronous oscillation detection in wind farm applications. Theapplication is targeted for real-time implementation at the edge, resulting insignificant savings in terms of data and communication requirements. Validationis performed using data from a North American wind farm operator.

A PMU-Based Machine Learning Application for Fast Detection of Forced Oscillations from Wind Farms