A Comparative Study of Data-Driven Power Grid Cascading Failure Prediction Methods
A Comparative Study of Data-Driven Power Grid Cascading Failure Prediction Methods
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DOI：
10.1109/naps58826.2023.10318537
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Nathalie Uwamahoro;Sara Eftekharnejad
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文献类型：
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作者：
Nathalie Uwamahoro;Sara Eftekharnejad
电网中的级联故障，其中故障从一个组件传播到另一个组件，是大规模停电的主要原因。随着人们对提高电网弹性的兴趣重新燃起，预测连锁故障变得更加重要，以便确定有效的缓解措施。现有的连锁故障预测方法缺乏高精度和快速的计算时间，往往面临着挑战，由于不平衡或不具有代表性的数据集。在这项工作中，各种数据驱动的方法进行故障预测与较短的计算时间的比较研究。该问题被制定为一个二元分类，其中的输入功能，如负载水平的传输线，被映射到输出，这是传输线的故障状态。为了验证所提出的方法的有效性，IEEE 30节点系统作为测试用例，结果证实了比较方法的可行性故障预测。该研究可以指导未来的研究开发快速，准确的数据驱动的连锁故障模型。
Cascading failures in power grids, where failures propagate from one component to another, are a major cause of large-scale blackouts. With renewed interest in enhancing power grid resilience, it is even more critical to predict cascading failures so that effective mitigative actions can be identified. The existing cascading failure prediction methods lack high accuracy and fast computation time and often face challenges due to unbalanced or unrepresentative datasets. In this work, a comparative study of various data-driven methods for failure prediction with a shorter computation time is provided. The problem is formulated as a binary classification, where the input features, such as the loading levels of the transmission lines, are mapped to the output, which is the failure status of the transmission lines. To validate the effectiveness of the proposed methods, the IEEE 30- bus system is used as a test case, and the results confirm the viability of the compared methods for failure prediction. This study could guide future research in developing fast and accurate data-driven cascading failure models.