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

Ruoyu Zhang,Baoqin Li,Junyong Wu,Yan Xu,Meiyang Shao

doi:10.3390/en12173217

Ruoyu Zhang, Baoqin Li + Show 3 more

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https://doi.org/10.3390/en12173217

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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.

Highlights

Transient stability or large-disturbance rotor angle stability is referred to as the ability of an interconnected power system to maintain synchronism when subjected to a large disturbance, such as a three-phase short-circuit fault on a transmission line [1]
The uncertain instances are recognized as credible instances at longer response times
Through the above hierarchical self-adaptive transient stability prediction, credible stable and unstable instances were exported at each decision time

Summary

Introduction

Transient stability or large-disturbance rotor angle stability is referred to as the ability of an interconnected power system to maintain synchronism when subjected to a large disturbance, such as a three-phase short-circuit fault on a transmission line [1]. Transient stability assessment (TSA) has significant importance in security monitoring of power systems. It is an essential requirement to maintain transient stability in power system operation. Through offline training on massive sets of data generated through time-domain (T-D) simulation, it can capture the potential useful knowledge to map the relationship between inputs (features of power system) and outputs (the corresponding dynamic security indices, such as stability status or stability margin/degree).

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