An Automated and Interpretable Machine Learning Scheme for Power System Transient Stability Assessment

Fang Liu, Xiaodi Wang, Ting Li, Mingzeng Huang, Tao Hu (), Yunfeng Wen and Yunche Su
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Fang Liu: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Xiaodi Wang: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Ting Li: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Mingzeng Huang: Engineering Research Center of Power Transmission and Transformation Technology of Ministry of Education, Hunan University, Changsha 410082, China
Tao Hu: Engineering Research Center of Power Transmission and Transformation Technology of Ministry of Education, Hunan University, Changsha 410082, China
Yunfeng Wen: Engineering Research Center of Power Transmission and Transformation Technology of Ministry of Education, Hunan University, Changsha 410082, China
Yunche Su: State Grid Sichuan Economic Research Institute, Chengdu 610041, China

Energies, 2023, vol. 16, issue 4, 1-16

Abstract: Many repeated manual feature adjustments and much heuristic parameter tuning are required during the debugging of machine learning (ML)-based transient stability assessment (TSA) of power systems. Furthermore, the results produced by ML-based TSA are often not explainable. This paper handles both the automation and interpretability issues of ML-based TSA. An automated machine learning (AutoML) scheme is proposed which consists of auto-feature selection, CatBoost, Bayesian optimization, and performance evaluation. CatBoost, as a new ensemble ML method, is implemented to achieve fast, scalable, and high performance for online TSA. To enable faster deployment and reduce the heavy dependence on human expertise, auto-feature selection and Bayesian optimization, respectively, are introduced to automatically determine the best input features and optimal hyperparameters. Furthermore, to help operators understand the prediction of stable/unstable TSA, an interpretability analysis based on the Shapley additive explanation (SHAP), is embedded into both offline and online phases of the AutoML framework. Test results on IEEE 39-bus system, IEEE 118-bus system, and a practical large-scale power system, demonstrate that the proposed approach achieves more accurate and certain appropriate trust solutions while saving a substantial amount of time in comparison to other methods.

Keywords: transient stability; automated machine learning; interpretability; Bayesian optimization; SHAP; CatBoost; PMU (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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