Author(s)

Jingnuo Wu ¹, Yao Xiao ¹

Affiliation(s)

¹ State Grid Tong Hua Power Supply Company, State Grid Jilin Electric Power Company Limited, Tonghua, Jilin, China

Corresponding Author

Yao Xiao

ABSTRACT

Nowadays, existing fault diagnosis technologies have problems such as slow response speed, low accuracy, and weak adaptive ability. To prevent overfitting, this article can use a strictly separated set of training and testing samples to train the model. In order to ensure the generalization performance of the model, mutual confirmation technology was adopted. The computing power of GPUs can be utilized to effectively process massive amounts of data and improve training efficiency. In the field of fault diagnosis, the proposed method can achieve real-time collection of the operating status of the power grid, and use the established artificial intelligence model to analyze it, thereby achieving rapid identification and localization of system fault types and locations. This method has self-learning function, which can continuously improve the accuracy of fault diagnosis while accumulating data. At the same time, the algorithm also has an alarm function, which can predict and warn the system before it malfunctions, thereby taking corresponding preventive measures. At a transmission speed of 10 kbps, the error detection accuracy of the system reached 98.5%. This article can promote the development of power grid fault diagnosis and protection technology, which is conducive to providing new ideas and methods for power system fault diagnosis and relay protection.

KEYWORDS

Artificial Intelligence, Power System Fault Diagnosis, Relay Protection Technology, Real-Time Prediction

CITE THIS PAPER

Jingnuo Wu, Yao Xiao, Artificial Intelligence Based Fault Diagnosis and Relay Protection Technology in Power Systems. Journal of Electrotechnology, Electrical Engineering and Management (2024) Vol. 7: 38-46. DOI: http://dx.doi.org/10.23977/jeeem.2024.070206.

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