Author(s)

Renjie Mao ¹, Jing Qiu ¹

Affiliation(s)

¹ The University of Sydney, Camperdown NSW, 2050, Australia

Corresponding Author

Renjie Mao

ABSTRACT

In recent years, with the increasing scale and complexity of power systems, traditional fault diagnosis and stability assessment methods have been unable to meet the actual needs. Therefore, it is of great significance to use machine learning technology to solve the problem of fault diagnosis and transient stability evaluation of power system. In the power system fault diagnosis, machine learning algorithm can automatically identify and predict the possible fault types in the power system by learning and analyzing a large number of historical fault data, improve the accuracy and efficiency of fault diagnosis, and reduce the impact of faults on the power system. In view of this, based on the power system fault diagnosis method and the power system transient stability evaluation method, the power system fault diagnosis and transient stability evaluation under the background of machine learning are deeply studied.

KEYWORDS

Machine learning; Electric power system; Fault diagnosis; Transient stability assessment

CITE THIS PAPER

Renjie Mao, Jing Qiu, Research on Fault Diagnosis and Transient Stability Evaluation of Power System Based on Machine Learning. Journal of Electrotechnology, Electrical Engineering and Management (2023) Vol. 6: 37-44. DOI: http://dx.doi.org/10.23977/jeeem.2023.060505.

REFERENCES

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[3] Shi Xuetao, Sun Haonan, Xiao Wenbiao et al. Application of Artificial Intelligence Technology in Power System Fault Diagnosis [J]. Electronic Technology and Software Engineering, 2021(07):221-222. 
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