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- W4387163670 abstract "At present, voltage stability has become an important factor threatening the security and stability of the power grid. For the prediction problem of voltage stability margin, an XGBoost regression prediction model is constructed and the grid state quantity is selected as the input feature. Through the results of the arithmetic test, it is found that the XGBoost regression prediction model is more accurate and faster than other common machine learning algorithms in terms of accuracy and calculation speed. Based on this, the fault tolerance of the model is investigated for possible transmission errors of grid information under actual operating conditions. The results show that the XGBoost regression model can still obtain better prediction results when there are data deviations; and by drawing violin plots of the prediction results for comparison, it is found that the XGBoost model has smaller prediction residuals and better performance." @default.
- W4387163670 created "2023-09-30" @default.
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- W4387163670 date "2023-08-18" @default.
- W4387163670 modified "2023-09-30" @default.
- W4387163670 title "XGBoost-based Voltage Stability Margin Prediction for Power Systems" @default.
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- W4387163670 doi "https://doi.org/10.1109/icsece58870.2023.10263428" @default.
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