Optimized method for testing equipment sensitivity to voltage sags and interruptions based on DC link voltage prediction

Determining equipment sensitivity and tolerance curves for voltage sags is essential for identifying vulnerable equipment and planning preventive maintenance. However, existing test procedures are repetitive and labor-intensive. To address this, this paper proposes a method to automatically predict equipment malfunction and reconstruct tolerance curves specifically for voltage sags and short interruptions, based on DC link voltage prediction. First, extensive sensitivity testing was conducted using existing testing methods to understand equipment behavior and establish malfunction criteria for voltage sags. The probabilistic range of tolerance curves was obtained by considering a wide range of sag scenarios. Equipment malfunction was observed when the minimum voltage across the DC link capacitor fell below the required operational level. Based on this established malfunction criteria, an optimized method was proposed, which requires only a few known nameplate parameters and a few initial tests. It achieves high prediction accuracy for DC link voltage across all cases (with an R-squared value of up to 1 and a mean absolute percentage error of <5 %). Additionally, the reconstructed tolerance curves obtained by the proposed method closely align with the measured tolerance curves, validating the effectiveness of the proposed method.