中圖分類(lèi)號(hào)：TM744 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.233729 中文引用格式： 龍玉江,，衛(wèi)薇，舒彧. 基于遺傳算法的輸變電設(shè)備數(shù)據(jù)補(bǔ)全[J]. 電子技術(shù)應(yīng)用,，2023,，49(9)：74-79. 英文引用格式： Long Yujiang，Wei Wei,，Shu Yu. Data completion of power transmission and transformation equipment based on genetic algorithm[J]. Application of Electronic Technique,，2023，49(9)：74-79.
Data completion of power transmission and transformation equipment based on genetic algorithm
Long Yujiang,，Wei Wei,，Shu Yu
(Guizhou Power Grid Co.,， Ltd. Information Center，Guiyang 550003,，China)
Abstract： With the development of digital twin technology, the power grid industry in China is also gradually developing from the original physical power grid to the digital power grid. Power transmission and transformation equipment, as the pivot equipment of electric energy transmission and transmission in the power grid, the reliability of its operation is directly related to the safe and stable operation of the power grid. Therefore, it is of great significance to grasp the current operating status of the power transmission and transformation equipment and the operating trend of the future period of time, and to achieve an accurate evaluation of the equipment operating status, to ensure the safe and reliable operation of the equipment. However, in the current practical application process, limited by the poor stability of the sensing device, the harsh on-site operating environment, and the complex electromagnetic environment, the state quantity data of the power transmission and transformation equipment will be missing data, resulting in poor data quality. This directly affects the accuracy of the equipment condition assessment model. In this paper, a genetic algorithm-based method for missing data completion of power transmission and transformation equipment is proposed. The method first randomly assigns values to the transform domain, and then achieves the effect of missing point recovery by minimizing the coefficient vector in the sparse domain. Experiments show that the algorithm can recover missing data accurately.
Key words : digital twin,；digital power grid；state assessment of power transmission and transformation equipment,；data completion,；genetic algorithm
