A Machine Learning-Based Reliability Evaluation Model for Integrated Power-Gas Systems

doi:10.1109/TPWRS.2021.3125531

A Machine Learning-Based Reliability Evaluation Model for Integrated Power-Gas Systems

2022-07-01

18

OA

AI

Mohammad Shahidehpour

DOI：10.1109/TPWRS.2021.3125531

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

Reliability

Power system reliability

Load modeling

Natural gas

Power systems

Planning

Measurement

Integrated Power-Gas System (IPGS)

machine learning

reliability evaluation

uncertainty

A Machine Learning-Based Reliability Evaluation Model for Integrated Power-Gas Systems

Abstract

Journal

Organization