Support Vector Machine For Transient Stability Assessment: A Review

TL;DR

This paper reviews the use of support vector machines for transient stability assessment in power systems, highlighting their potential for real-time, accurate, and fast analysis amidst increasing system uncertainties.

Contribution

It provides a comprehensive review of support vector machine applications in transient stability assessment, emphasizing their advantages over traditional methods.

Findings

Support vector machines offer high accuracy for transient stability assessment.

SVM-based methods enable faster computation suitable for real-time applications.

The review highlights the growing importance of machine learning in power system stability analysis.

Abstract

Accurate transient stability assessment is a crucial prerequisite for proper power system operation and planning with various operational constraints. Transient stability assessment of modern power systems is becoming very challenging due to rising uncertainty and continuous integration of renewable energy generation. The stringent requirements of very high accuracy and fast computation speed has further necessitated accurate transient stability assessment for power system planning and operation. The traditional approaches are unable to fulfil these requirements due to their shortcomings. In this regard, the popularity of prospective approaches based on big data and machine learning, such as support vector machine, is constantly on the rise as they have all the features required to fulfil important criteria for real-time TSA. Therefore, this paper aims to review the application of support vector machine for transient stability assessment of power systems. It is believed that this work will provide a solid foundation for researchers in the domain of machine learning and computational intelligence-based applications to power system stability and operation.