Transient stability assessment (TSA) is of great importance in power system operation and control. One of the usual tasks in TSA is to estimate the critical clearing time (CCT) of a given fault under the given network topology and pre-fault power flow. Data-driven methods try to obtain models describing the mapping between these factors and the CCT from a large number of samples. However, the influence of network topology on CCT is hard to be analyzed and is often ignored, which makes the models inaccurate and unpractical. In this paper, a novel data-driven TSA model combining Mahalanobis kernel regression and ensemble learning is proposed to deal with the problem. The model is a weighted sum of several sub-models. Each sub-model only uses the data of one topology to construct a kernel regressor. The weights are determined by both the topological similarity and numerical similarity between the samples. The similarities are decided by the parameters in Mahalanobis distance, and the parameters are to be trained. To reduce the model complexity, sub-models within the same topology category share the same parameters. When estimating CCT, the model uses not only the sub-model which the sample topology belongs to, but also other sub-models. Thus, it avoids the problem that there may be too few data under some topologies. It also efficiently utilizes information of data under all the topologies. Moreover, its decision-making process is clear and understandable, and an effective training algorithm is also designed. Test results on both the IEEE 10-machine 39-bus and a real system verify the effectiveness of the proposed model.

中文翻译：

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通过Mahalanobis核回归和集成学习考虑网络拓扑变化的数据驱动暂态稳定性评估模型

暂态稳定评估（TSA）在电力系统的运行和控制中非常重要。TSA中的常见任务之一是在给定的网络拓扑和故障前的潮流下估算给定故障的关键清除时间（CCT）。数据驱动的方法试图从大量样本中获得描述这些因素与CCT之间映射的模型。但是，网络拓扑对CCT的影响很难分析，并且经常被忽略，这使得模型不准确且不切实际。本文提出了一种新的数据驱动的TSA模型，该模型结合了Mahalanobis核回归和集成学习来解决该问题。该模型是几个子模型的加权和。每个子模型仅使用一种拓扑的数据来构造内核回归器。权重由样本之间的拓扑相似性和数值相似性决定。相似性由马氏距离的参数决定，这些参数需要训练。为了降低模型的复杂性，相同拓扑类别内的子模型共享相同的参数。估计CCT时，该模型不仅使用样本拓扑所属的子模型，还使用其他子模型。因此，它避免了某些拓扑下的数据可能太少的问题。它还可以有效利用所有拓扑下的数据信息。此外，其决策过程清晰易懂，并设计了有效的训练算法。在IEEE 10机39总线和真实系统上的测试结果验证了所提出模型的有效性。