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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper, the authors present the calibration of the parameter of arc furnace that considers the non-linearity and high variability of this type of load. Starting with a nonlinear differential equation that describes the voltage-current characteristic of the arc, an equivalent linear equation that simplifies the estimation of the original model parameters is proposed. Parameter estimation is then accomplished using Bayesian linear regression using measurements taken during the furnance's most critical operation point. Relationships between the estimated value for the parameters and their uncertainty, in terms of the number of observations included in the model calibration process, are shown. Results obtained through simulation with the estimated parameters are contrasted against real data. A flicker meter, which complies with the IEC standard 61000-4-15, is used for determining the instantaneous flicker level (IFL) due to fluctuations present in the real and simulated current waveforms. Finally, the harmonic content of the real and simulated current waveforms is compared.

Regresión Bayesiana lineal para calibrar los parámetros de un modelo de horno de arco