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A Machine Learning-Based Reliability Evaluation Model for Integrated Power-Gas Systems
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DOI：10.1109/TPWRS.2021.3125531.png)
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摘要
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This paper proposes a machine learning method for the reliability evaluation of integrated power-gas systems (IPGS) under the uncertain component failure probability distributions. The Random Forest (RF) method is designed to select important features to solve the insufficient quantity of data and the curse of dimensionality problems. The Extreme Gradient Boosting (XGBoost) regression algorithm is developed to quantify the relationship between the uncertain parameters and reliability metrics. Moreover, a ten-fold cross-validation method is employed to further improve the accuracy of the regression model. Simulation results on three test systems show that the proposed method can achieve high accuracy for the reliability evaluation.
Keyword:
Reliability
Power system reliability
Load modeling
Natural gas
Power systems
Planning
Measurement
Integrated Power-Gas System (IPGS)
machine learning
reliability evaluation
uncertainty
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
机构
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