Research on Cross-Domain Defect Diagnosis and Model Generalization of Power Equipment for High-Penetration Distributed Generation Scenarios
DOI:
https://doi.org/10.13052/dgaej2156-3306.4155Keywords:
power equipment, fault diagnosis, Cross-Domain Generalization, Domain AdaptationAbstract
With the construction of the new power system, the penetration rate of distributed renewable energy in power distribution networks has gradually increased, bringing new characteristics to the new power system, including bidirectional power flow in equipment operation and frequent operating condition fluctuations. Faced with increasingly complex operating conditions and frequent fluctuations of equipment, the traditional defect diagnosis methods for power system equipment lack generalization ability for scenario migration. Therefore, this paper focuses on the defect diagnosis of power equipment and model generalization under the scenario of high penetration of distributed renewable energy. Based on the analysis of multi-source heterogeneous detection data, a cross-domain defect diagnosis framework integrating domain adaptation and feature alignment is constructed. Firstly, domain-invariant features under different operation scenarios are extracted, and combined with the data augmentation strategy, the problems of sparse and unbalanced distribution of typical defect samples in high-penetration scenarios are alleviated. Secondly, the results are verified based on multi-scenario platforms and measured data, and the diagnosis accuracy and transferability of the proposed method are compared with those of traditional models. The verification results demonstrate that the research in this paper can provide novel technical ideas for improving the state perception capability of power equipment in the complex distribution network environment of the new power system.
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