Review
A Critical Review of Data-Driven Transient Stability
Assessment of Power Systems: Principles, Prospects
and Challenges
Shitu Zhang
1
, Zhixun Zhu
2
and Yang Li
1,
*
Citation: Zhang, S.; Zhu, Z.; Li, Y. A
Critical Review of Data-Driven
Transient Stability Assessment of
Power Systems: Principles, Prospects
and Challenges. Energies 2021, 14,
7238. https://doi.org/10.3390/
en14217238
Academic Editors: Pierluigi Siano,
Hassan Haes Alhelou and
Amer Al-Hinai
Received: 25 August 2021
Accepted: 1 November 2021
Published: 2 November 2021
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1
School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China;
2202000277@neepu.edu.cn
2
GHN Energy Jilin Jiangnan Thermal Power Co., Ltd., Jilin 132013, China; 12052729@chnenergy.com.cn
* Correspondence: liyang@neepu.edu.cn
Abstract:
Transient stability assessment (TSA) has always been a fundamental means for ensuring
the secure and stable operation of power systems. Due to the integration of new elements such as
power electronics, electric vehicles and renewable power generations, dynamic characteristics of
power systems are becoming more and more complex, which makes TSA an increasingly urgent
task. Since traditional time-domain simulations and direct method cannot meet the actual opera-
tion requirements of power systems, data-driven TSA has attracted growing attention from both
academia and industry. This paper makes a comprehensive review from the following four aspects:
feature extraction and selection, model construction, online learning and rule extraction; and then,
summarizes the challenges and prospects for future research; finally, draws the conclusions of this
review. This review will be beneficial for relevant researchers to better understand the research status,
key technologies, and existing challenges in the field.
Keywords:
transient stability assessment; power systems; data-driven approach; feature extraction
and selection; model construction; review
1. Introduction
Transient stability assessment (TSA) is a fundamental means for ensuring the secure
and stable operation of power systems. Transient stability of power systems refers to the
ability of each generator in the system to maintain synchronous operation after a large
disturbance [
1
]. With the increasing penetration of new elements such as power electronics,
electric vehicles, and renewable power generations, dynamic characteristics of power
systems are becoming more and more complex. In this situation, accurate and rapid TSA
is increasingly urgent. With the rapid development of artificial intelligence techniques,
data-driven TSA approaches became a hot topic in recent years, and a large number of
research results were produced. Therefore, it is necessary to make a critical review of
existing data-driven TSA approaches so that relevant researchers can better understand
the research status, key technologies, and existing challenges in the field.
As summarized in Table 1, existing TSA methods can be roughly divided into three
categories: time-domain simulation method [
2
], direct method [
3
], and data-driven arti-
ficial intelligence (AI) method [
4
,
5
]. The basic idea of time-domain simulation methods
is to use a numerical integration algorithm to solve the differential-algebraic equations
(DAEs) describing the dynamic process of a disturbed power system, and then judge the
stability status of the system by the relative angle changes between generator rotors. Due to
good model adaptability and reliability, this method was widely used in the electric power
industry. Reference [
6
] proposes the application of the unsymmetric multifrontal method to
solve the DAEs encountered in the power system dynamic simulations. Reference [
7
] pro-
poses a time-domain simulation approach for power system dynamic simulations by using
Energies 2021, 14, 7238. https://doi.org/10.3390/en14217238 https://www.mdpi.com/journal/energies
