Evaluation method of substation equipment health status based on data unified semantic representation

Yijia Wu; Yisheng Yu; Guodong Li; Xingyu Tang; Biyan Yan

doi:10.1117/12.2656812

20 October 2022 Evaluation method of substation equipment health status based on data unified semantic representation

Yijia Wu, Yisheng Yu, Guodong Li, Xingyu Tang, Biyan Yan

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 1245137 (2022) https://doi.org/10.1117/12.2656812
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

Aiming at the problem of maintenance data quality of substation equipment in the power grid industry, an evaluation method of substation equipment health status based on unified semantic representation was proposed. Firstly, the current health data of substation equipment and historical data of integrated equipment archives are analyzed. Multi-source heterogeneous substation equipment data is processed by data governance and structured equipment health record database are constructed. Secondly, the selection and construction of data features are designed by observing the data scale of archives. Then, a unified semantic distributed representation model is constructed, and the word bag vector compressor is used to reconstruct the structured text data of the station into a standard state vector. Finally, a state evaluation method based on neural network is proposed to evaluate the state vector. Experiment result shows that the method proposed in this paper is feasible in the health status evaluation of substation equipment, and the accuracy rate is up to 80%.

Citation Download Citation

Yijia Wu, Yisheng Yu, Guodong Li, Xingyu Tang, and Biyan Yan "Evaluation method of substation equipment health status based on data unified semantic representation", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 1245137 (20 October 2022); https://doi.org/10.1117/12.2656812

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