Transient stability assessment model with parallel structure and data augmentation

Chen, Qifan; Lin, Nan; Wang, Huaiyuan

doi:10.1002/2050-7038.12872

Cited by 9 publications

(2 citation statements)

References 38 publications

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“…Its objective function, denoted as O bj , is composed of the model's loss function L and a regularization term that suppresses model complexity, as shown in Eq. (5).…”

Section: Improved Xgboost Model 31 Xgboost Algorithm With Regular Termsmentioning

confidence: 99%

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Migratable Power System Transient Stability Assessment Method Based on Improved XGBoost

Qu,

Wang,

Cheng

et al. 2024

ENERGY

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The data-driven transient stability assessment (TSA) of power systems can predict online real-time prediction by learning the temporal features before and after faults. However, the accuracy of the assessment is limited by the quality of the data and has weak transferability. Based on this, this paper proposes a method for TSA of power systems based on an improved extreme gradient boosting (XGBoost) model. Firstly, the gradient detection method is employed to remove noise interference while maintaining the original time series trend. On this basis, a focal loss function is introduced to guide the training of the XGBoost model, enhancing the deep exploration of minority class samples to improve the accuracy of the model evaluation. Furthermore, to improve the generalization ability of the evaluation model, a transfer learning method based on model parameters and sample augmentation is proposed. The simulation analysis on the IEEE 39-bus system demonstrates that the proposed method, compared to the traditional machine learning-based transient stability assessment approach, achieves an average improvement of 2.16% in evaluation accuracy. Specifically, under scenarios involving changes in topology structure and operating conditions, the accuracy is enhanced by 3.65% and 3.11%, respectively. Moreover, the model updating efficiency is enhanced by 14-15 times, indicating the model's transferable and adaptive capabilities across multiple scenarios.

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“…Its objective function, denoted as O bj , is composed of the model's loss function L and a regularization term that suppresses model complexity, as shown in Eq. (5).…”

Section: Improved Xgboost Model 31 Xgboost Algorithm With Regular Termsmentioning

confidence: 99%

“…With the rapid development of artificial intelligence technology and measurement tools such as phase measurement units (PMUs), it has been support for research on data-driven TSA of power systems. Many scholars have conducted research on algorithms and data [3][4][5]. The data-driven TSA mainly includes offline training and online mapping.…”

Section: Introductionmentioning

confidence: 99%