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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

With the transition to a smart grid, we are witnessing a significant growthin sensor deployments and smart metering infrastructure in the distributionsystem. However, information from these sensors and meters are typicallyunevenly sampled at different time-scales and are incomplete. It is critical toeffectively aggregate these information sources for situational awareness. Inorder to reconcile the heterogeneous multi-scale time-series data, we present amulti-task Gaussian process framework. This framework exploits thespatio-temporal correlation across the time-series data to impute data at anydesired time-scale while providing confidence bounds on the imputations. Thevalue of the imputed data for distribution system operation is illustrated viaa matrix completion based state estimation strategy. Results on the IEEE 37 busdistribution system reveals the superior performance of the proposed approachrelative to linear interpolation approaches.

Multi Time-scale Imputation aided State Estimation in Distribution System