Fast Detection of Current Transformer Saturation Using Stacked Denoising Autoencoders

Sopheap Key, Chang-Sung Ko, Kwang-Jae Song and Soon-Ryul Nam ()
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Sopheap Key: Department of Electrical Engineering, Myongji University, Yongin 17058, Republic of Korea
Chang-Sung Ko: Department of Electrical Engineering, Myongji University, Yongin 17058, Republic of Korea
Kwang-Jae Song: Department of Electrical Engineering, Myongji University, Yongin 17058, Republic of Korea
Soon-Ryul Nam: Department of Electrical Engineering, Myongji University, Yongin 17058, Republic of Korea

Energies, 2023, vol. 16, issue 3, 1-16

Abstract: Malfunctions in relay protection devices are predominantly caused by current transformer (CT) saturation which produces distortion in current measurements and disturbances in power system protection. The development of deep learning in power system protection is on the rise recently because of its robustness. This study presents a CT saturation detection where the secondary current becomes distorted. The proposed scheme offers a wide range of saturation detection and consists of a moving-window technique and stacked denoising autoencoders. Moreover, Bayesian optimization was used to minimize the difficulty of determining neural network structure for the proposed approach. The performance of the algorithm was evaluated for a-g faults on 154 kV and 345 kV overhead transmission line in South Korea. The waveform variation has been generated by PSCAD for different scenarios that heavily influence CT saturation. Moreover, a comparative analysis with other methods demonstrated the superiority of the proposed DNN method. With the proposed algorithm to detect CT saturation, it significantly yielded high accuracy and precision for CT saturation detection which were approximately 99.71% and 99.32%, respectively.

Keywords: current transformer; saturation; denoising autoencoders; detection; protection (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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