Abstract Transient stability assessment (TSA) plays an important role in the design and operation of power system. With the widespread deployment of phasor measurement units (PMUs), the machine learning-based method has attracted much attention for its speed and generalization. However, the generalization will deteriorate if some features are missing due to PMU failure. In this paper, a spatial-temporal adaptive TSA method is proposed to handle the missing data issue. By developing an optimal PMU clusters searching model based on temporal feature importance, and by constructing an ensemble mechanism of long short-term memory (LSTM) for the optimal PMU clusters, the spatial-temporal information is utilized adaptively. Therefore, the aim of maintaining the robustness of TSA performance under any possible PMU failure event is achieved. The proposed approach is demonstrated on New England 39-bus power system. Compared with existing methods, the proposed method achieves state-of-art performance in both accuracy and response time under missing data conditions. In addition, the proposed method is more robust in the case of PMU failure than others.

中文翻译：

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缺失数据下电力系统时空自适应暂态稳定性评估

摘要 暂态稳定性评估（TSA）在电力系统的设计和运行中具有重要作用。随着相量测量单元（PMU）的广泛部署，基于机器学习的方法因其速度和泛化性而备受关注。但是，如果由于 PMU 故障而丢失了某些特征，则泛化会变差。在本文中，提出了一种时空自适应TSA方法来处理缺失数据问题。通过开发基于时间特征重要性的最优 PMU 集群搜索模型，并通过为最优 PMU 集群构建长短期记忆 (LSTM) 的集成机制，自适应地利用时空信息。因此，达到了在任何可能的 PMU 故障事件下保持 TSA 性能稳健性的目的。所提出的方法在新英格兰 39 母线电力系统上进行了演示。与现有方法相比，所提出的方法在丢失数据的情况下在准确性和响应时间方面都达到了最先进的性能。此外，所提出的方法在 PMU 故障的情况下比其他方法更稳健。