基于轨迹簇和MBLDA的受端电网暂态电压稳定评估

Transient voltage stability evaluation of a receiving-end power grid based on a trajectory cluster and MBLDA

针对现有暂态电压状态的多样化以及暂态电压稳定模型训练速度有待进一步提升的问题,提出一种基于电压轨迹簇和多类间线性判别分析(Multiple Between-class Linear Discriminant Analysis,MBLDA)的交直流系统暂态电压稳定评估方法。首先,获取故障后系统关键节点电压受扰轨迹簇信息,借助轨迹簇的几何属性建立暂态电压稳定评估的原始特征集。进而采用ReliefF算法对原始特征集进行压缩,筛选出与系统暂态电压稳定状态密切相关的特征子集,有效表征暂态电压稳定的四种状态(电压迅速恢复、电压延迟恢复、持续低电压、电压振荡)。然后,将高维电压特征空间的特征值方程转化为欠定齐次方程组,提高暂态电压稳定评估模型的训练速度,进而建立大规模系统电压特征集与4种暂态电压稳定状态的映射关系。最后,通过修改后的IEEE39节点系统和修改后的IEEE145节点系统的仿真分析,验证所提方法的可行性与有效性。

In view of the diversification of the existing transient voltage state and the problem that the training speed of a transient voltage stability model needs to be further improved,this paper proposes a method for evaluating the transient voltage stability of AC and DC systems based on voltage trajectory cluster theory and Multiple Between-Class Linear Discriminant Analysis(MBLDA).First,this paper obtains the information of the trajectory clusters of the voltage disturbance of each node of the system after a fault,and establishes the original feature set for transient voltage stability evaluation using the geometric properties of the trajectory clusters.It uses ReliefF to perform feature compression on the original feature set,screens out the subset of features closely related to the transient voltage stability of the system,and effectively represents the four types of transient voltage stability status(fast recovery,delayed recovery,continuous low,and voltage oscillation).Then,the eigenvalue equations of the high-dimensional feature space are transformed into underdetermined homogeneous equations to improve the training speed of the transient voltage stability evaluation model.Then we establish the mapping relationship between the large-scale system voltage feature set and the four transient voltage stability states.Finally,the feasibility and effectiveness of the proposed method are verified by simulation analysis of the modified IEEE-39 node system and the modified IEEE145 node system.