基于遗传算法的输变电设备数据补全

Data completion of power transmission and transformation equipment based ongenetic algorithm

在数字孪生技术的发展下,我国电网行业也在由原来的物理电网逐步向数字电网发展。输变电设备作为电网中的电能输送和传输的枢纽设备,其运行的可靠性直接关系到电网的安全稳定运行。因此,及时掌握输变电设备当前的运行状态以及未来一段时间的运行趋势,实现对设备运行状态的准确评估,对于保证设备安全可靠运行具有重要意义。然而,在目前的实际应用过程中,受限于传感装置稳定性差、现场运行环境恶劣、电磁环境复杂等原因,输变电设备的状态量数据会出现数据缺失造成获得的数据质量较差,这会直接影响设备状态评估模型的准确性。提出一种基于遗传算法的输变电设备缺失数据补全方法,首先对变换域随机赋值,然后通过实现稀疏域中系数向量最小化达到缺失点恢复的效果。实验证明,该算法能够准确地恢复缺失数据。

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.