How to estimate the damping of power system?
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Estimating the damping of power systems can be done using various approaches. One method is to discard the first several swings of measurements to avoid nonlinearity, but this can lead to varying damping ratio estimates depending on the measuring window used . Another approach is to utilize complete post-disturbance data and fit a nonlinear oscillator to a dominant mode, providing robust damping estimation independent of the measuring window . Additionally, a framework for designing a multisine probing signal applied to power electronics-based grid actuators can provide accurate damping estimation with user-specified variance . Machine learning algorithms such as SVM, random forest, and XGBoost can also be used to predict damping percentage based on data obtained from Phasor Measurement Units (PMUs) . Furthermore, a data-driven model-predictive control approach, utilizing dynamic mode decomposition with control (DMDc) and an extended state observer (ESO), can be used to estimate and mitigate disturbances for oscillation damping in power systems .
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| Papers (4) | Insight |
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| The paper uses three machine learning algorithms (SVM, random forest, and XGBoost) to predict the damping percentage of a power system due to inter-area modes of oscillations. | |
7 Citations | The paper proposes a new measurement-based approach utilizing complete post-disturbance data for robust damping estimation in power systems. |
07 Sep 2020 2 Citations | The paper proposes a framework for designing a multisine probing signal that, when applied to the control inputs of a power electronics-based grid actuator, can provide an estimation of power system damping with a user-specified variance. |
| The paper proposes a new measurement-based approach utilizing complete post-disturbance data for robust damping estimation in power systems. |
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