基于多阶段协同的电网厂站接线图智能识别

电网厂站接线图作为电力系统网络结构的重要数据,承载着电力设备运行的关键信息,在电网调度控制应用中具有重要作用。针对调度运维人员手动绘制方式导致工作繁琐、任务量大的问题,提出基于多阶段协同的电网厂站接线图智能识别方法,实现机器代人绘图。首先,采用切割检测增强对图元小目标的检测能力,通过形态特征提取及模糊比对处理实现图元方向识别;其次,基于随机粘贴原理增强文字识别模型的鲁棒性,基于知识融合技术提升图元文本匹配准确性;最后,基于连通域检测识别连接线,采用遍历筛选匹配图元与主接线的连接关系。实验结果表明,该方法对电网厂站接线图整体识别准确率在88%以上,将其应用变电站监控系统、换流站运行人员控制系统中,极大提升了作业人员构图建模效率。

Nonlinear identification and characterization of structural joints based on vibration transmissibility

In order to investigate the nonlinear characteristics of structural joint,the experimental setup with a jointed mass system is established for dynamic characterization analysis and vibration prediction,and a corresponding nonlinearity identification method is studied.First,the sine-sweep vibration test with different baseexcitation levels areapplied to the structural joint system to study the dominant modal of mass rigid motion.Then,based on t e harmonic balance method principle,t e measured vibration transmissibilities a e utilized for nonlinearity identification using different excitation levels.Experimental results show that nonlinear spring and damping force can be represented by a polynomial order approximation.The identified nonlinear stiffness and damping force can predict the system’s response,and they can reveal t e shifts of resonant frequency or damping due to discontinuity of contact mechanisms within a certain range.