Power System Transient Stability Assessment Based on Intelligent Enhanced Transient Energy Function Method

Tianxiao Mo, Jun Liu (), Jiacheng Liu, Guangyao Wang, Yuting Li and Kaiwei Lin
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Tianxiao Mo: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Jun Liu: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Jiacheng Liu: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Guangyao Wang: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Yuting Li: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Kaiwei Lin: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Energies, 2024, vol. 17, issue 23, 1-16

Abstract: The development of power systems puts forward higher requirements for transient stability evaluations of power systems. The accuracy and timeliness of transient stability assessment are of great significance to the safe and stable operation of power systems. Traditional mechanistic judgment methods and criteria have strong interpretability, but they also have great limitations. They are still difficult to apply to complex power systems and are in urgent need of improvement. Artificial intelligence methods have high accuracy in stability judgment, but they have problems such as poor interpretability, and their stability judgment results are often difficult to explain. Based on the transient stability judgment mechanism of the response-driven transient energy function, this paper proposes a transient energy function stability judgment method based on a two-machine equivalent model and enhanced by a convolutional neural network. Firstly, the ST-kmeans method is used to cluster the generator sets, and the S-transformation is performed on the power angle changes of the generator sets to extract features. Then, the principal component analysis method is used to reduce the dimension of the feature data. Based on the k-means clustering method, the IEEE-39 node system generator synchronization units are grouped according to the power angle change trend of each generator after the fault. On the basis of the above methods, a two-machine equivalent model of the IEEE-39 node system is established, and the transient energy function of the two-machine system is derived. Based on the convolutional neural network, the critical energy is enhanced, and the fixed critical energy threshold is replaced by the corrected critical energy. The example results show that the transient stability prediction framework proposed in this paper can improve the scope of the application of mechanism discrimination and enhance the interpretability of the results of the intelligent method.

Keywords: transient stability assessment; transient energy function; ST-kmeans; intelligence augmentation; interpretability (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: 2024
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