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Keywords

protective device, state evaluation, state earlywarning, generating adversarial network, random forest

Abstract

To improve the status evaluation accuracy of protection systems, a smart status evaluation method based on GAN model and random forest algorithm is proposed. Firstly, a system state indicator set is established in combination of the field conditions and expert opinions. To address the problem of the imbalance of relay protection equipment state data, a state data generation method is proposed based on the generation countermeasure network. Then, a comprehensive evaluation model of protection systems based on random forest is established. Finally, combining with the preceding state evaluation results, the health index curve of the equipment and its deterioration trend are given to provide corresponding state earlywarning. The realdata experimental results show that this method can more accurately evaluate the system status, and has reference value for rationally arranging the maintenance cycle and formulating the maintenance plan.

DOI

10.19781/j.issn.1673-9140.2021.06.012

First Page

104

Last Page

112

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