雷电引起的电压暂降严重程度自学习评估方法

雷击故障是造成电网电压暂降的主要原因之一，准确评估雷电造成的电压暂降严重程度可以为制定最优治理方案和敏感用户选址提供依据。文中提出一种数据驱动的电压暂降严重程度自学习评估方法。首先，基于雷电造成的电压暂降机理，结合雷电定位系统和电能质量监测系统中的监测信息选取参与挖掘的参数；其次，减少离散化结果对规则准确性的影响，使用离散化评价系数确定不同参数的离散区间数目；然后，针对电网数据库动态变化时挖掘算法效率过低的问题，使用基于增量式学习的关联规则挖掘算法持续更新挖掘规则，从而赋予其自学习的能力；最后，提出基于综合赋权法的加权欧氏距离评估实际场景的电压暂降严重程度。通过某地区电网的监测数据和IEEE 30节点系统仿真数据进行实证分析，结果证明文中方法能在实际应用中准确挖掘有价值规则，实现关注节点的电压暂降严重程度评估。

Self-learning estimation method for the severity of voltage sags caused by lightning

Lightning is one of the main causes of voltage sags in the power grid. Accurate estimation of the severity of voltage sags caused by lightning can provide a basis for developing optimal management plans and siting sensitive users. In this paper, we propose a data-driven self-learning estimation method for voltage sag severity. Firstly, based on the mechanism of voltage sags caused by lightning, the parameters involved in mining are selected by the monitoring information in lightning location system and power quality monitoring system. Secondly, the influence of discretization results on the accuracy of rules is reduced, and the number of discretization intervals for different parameters is determined by using discretization evaluation indexes. Then, to solve the problem of low efficiency of mining algorithm when the grid database changes dynamically, the incremental learning-based association rule mining algorithm to continuously update the mined rules, which gives it the ability of self-learning. Finally, a weighted Euclidean distance based on the integrated assignment method is proposed to evaluate the severity of voltage sags in real scenarios. The results of the empirical analysis by monitoring data of a regional power grid and simulation data of IEEE30 nodes prove that the method in this paper can accurately mine valuable rules in reality and realize the estimation of voltage sag severity of the concerned nodes.