A Hierarchical Self-Adaptive Method for Post-Disturbance Transient Stability Assessment of Power Systems Using an Integrated CNN-Based Ensemble Classifier

Ruoyu Zhang, Junyong Wu, Yan Xu, Baoqin Li and Meiyang Shao
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Ruoyu Zhang: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Junyong Wu: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Yan Xu: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Baoqin Li: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Meiyang Shao: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China

Energies, 2019, vol. 12, issue 17, 1-20

Abstract: Data-driven approaches using synchronous phasor measurements are playing an important role in transient stability assessment (TSA). For post-disturbance TSA, there is not a definite conclusion about how long the response time should be. Furthermore, previous studies seldom considered the confidence level of prediction results and specific stability degree. Since transient stability can develop very fast and cause tremendous economic losses, there is an urgent need for faster response speed, credible accurate prediction results, and specific stability degree. This paper proposed a hierarchical self-adaptive method using an integrated convolutional neural network (CNN)-based ensemble classifier to solve these problems. Firstly, a set of classifiers are sequentially organized at different response times to construct different layers of the proposed method. Secondly, the confidence integrated decision-making rules are defined. Those predicted as credible stable/unstable cases are sent into the stable/unstable regression model which is built at the corresponding decision time. The simulation results show that the proposed method can not only balance the accuracy and rapidity of the transient stability prediction, but also predict the stability degree with very low prediction errors, allowing more time and an instructive guide for emergency controls.

Keywords: Transient stability assessment (TSA); intelligent system (IS); convolutional neural network (CNN); ensemble learning; hierarchical self-adaptive method; integrated decision-making rule; power systems (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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