Author(s)

Ming Gao ¹, Feng Zheng ¹

Affiliation(s)

¹ Tongji University Yangpu District, Shanghai, China

Corresponding Author

Ming Gao

ABSTRACT

This paper discusses the key technologies and existing issues in fault diagnosis and prediction of power equipment in smart grids, and proposes corresponding optimization strategies. In terms of data processing technology, solutions are proposed for data quality issues, including data cleaning and missing value imputation, data augmentation and smoothing, as well as efficient data transmission and storage schemes. In terms of algorithm model optimization, the accuracy and robustness of fault diagnosis and prediction are improved through the design of lightweight and efficient algorithms, model fusion and ensemble learning, as well as adaptive and online learning methods. In terms of system integration and application optimization, the compatibility, real-time performance, and security of the system are enhanced through standardized and modular design, establishment of real-time monitoring and response systems, and implementation of safety protection and privacy mechanisms, ensuring the safe and stable operation of smart grids.

KEYWORDS

Smart grid; power equipment; fault diagnosis; data processing technology

CITE THIS PAPER

Ming Gao, Feng Zheng, Research on Fault Diagnosis and Prediction Algorithms for Power Equipment in Smart Grids. Journal of Electronics and Information Science (2024) Vol. 9: 114-119. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2024.090214.

REFERENCES

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[3] Niu Ziyi, Liu Hao, He Wei, et al. A Distributed Energy Management System for Smart Grid Based on Cloud Computing and Big Data. CN202110141419.9[2024-06-11].
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