The increase in demand and generator reaching reactive power limits may operate the power system in stressed conditions leading to voltage instability. Thus, the voltage stability assessment is essential for estimating the loadability margin of the power system. The grid operators urgently need a voltage stability assessment (VSA) method with high accuracy, fast response speed, and good scalability. The static VSA problem is defined as a regression problem. Moreover, an artificial neural network is constructed for online assessment of the regression problem. Firstly, the training sample set is obtained through scene simulation, power flow calculation, and local voltage stability index calculation; then, the class imbalance problem of the training samples is solved by the random under-sampling bagging (RUSBagging) method. Then, the mapping relationship between each feature and voltage stability is obtained by an artificial neural network. Finally, taking the modified IEEE39 node system as an example, by setting up four groups of methods for comparison, it is verified that the proposed method has a relatively ideal modeling speed and high accuracy, and can meet the requirements of power system voltage stability assessment.

中文翻译：

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使用随机欠采样 Bagging BP 方法进行静态电压稳定性评估

需求的增加和发电机达到无功功率限制可能会在导致电压不稳定的压力条件下运行电力系统。因此，电压稳定性评估对于估计电力系统的负载能力裕度至关重要。电网运营商迫切需要一种精度高、响应速度快、可扩展性好的电压稳定性评估（VSA）方法。静态 VSA 问题被定义为回归问题。此外，构建了一个人工神经网络，用于回归问题的在线评估。首先，通过场景模拟、潮流计算、局部电压稳定指标计算得到训练样本集；然后，通过随机欠采样装袋（RUSBagging）方法解决训练样本的类不平衡问题。然后，通过人工神经网络获得每个特征与电压稳定性的映射关系。最后以修改后的IEEE39节点系统为例，通过设置四组方法进行对比，验证了所提方法具有较为理想的建模速度和较高的精度，能够满足电力系统电压稳定性评估的要求。 .