The Research of SVG Based on the Unbalanced Power System

Yue, Gaili; Li, Xun; Sui, Xiaona

doi:10.1109/is3c.2016.220

Cited by 4 publications

(2 citation statements)

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“…() ( , ) ( ) 11 (6) XGBoost expands the objective function to the second-order form using the Taylor expansion formula, as shown in Equation (7). The model predictions of the loss of SVG are obtained by summing the output weights of each leaf node.…”

Section: Xgboost-based Svg Loss Estimationmentioning

confidence: 99%

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SVG Loss Estimation Method Based on Extreme Gradient Boosting

Xiao,

Huang

2023

J. Phys.: Conf. Ser.

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Static Var Generator (SVG) consumes large amounts of electrical energy during its operation, generating high equipment operating costs. Therefore, it is necessary to quantify the operating SVG loss. However, the existing methods of loss estimation generally have notable problems, such as large deviations in loss estimation results. In this paper, we propose a method for calculating SVG loss and establish an SVG loss estimation model based on the extreme gradient boosting algorithm under different operating conditions. First, some data obtained by simulation experiments are input into the algorithm model for training, and the hyperparameters of the model are adjusted by the Bayesian optimization algorithm. Then, a new test set is constructed by superimposing the measurement errors with different signal-to-noise ratio noises on the test set data. Finally, the test set is fed into the trained model for testing to validate the effect of the present model. The experimental results show that the model can quickly and accurately achieve loss estimation of SVG devices with good engineering practicality.

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Section: Xgboost-based Svg Loss Estimationmentioning

confidence: 99%

“…The model predictions of the loss of SVG are obtained by summing the output weights of each leaf node. [6]. Furthermore, the model introduces the time-varying resistance to simulate the loss change.…”

Section: Xgboost-based Svg Loss Estimationmentioning

confidence: 99%