Machine Learning-Based Cascade Size Prediction Analysis in Power Systems
Machine Learning-Based Cascade Size Prediction Analysis in Power Systems
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DOI：
10.1109/naps58826.2023.10318644
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Naeem Md Sami;M. Naeini
中科院分区：
文献类型：
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作者：
Naeem Md Sami;M. Naeini
连锁故障虽然不是很常见，但仍然是电力系统中令人担忧的问题，这可能导致巨大的电力服务中断，给社会带来巨大的代价。人们进行了广泛的研究，以了解这些复杂的现象并减轻其影响。随着大量能源数据的可获得性，数据驱动和基于机器学习(ML)的技术应运而生，以支持对梯级弹性电力系统的努力。预测连锁故障的风险和规模是支持控制和缓解此类事件的其他基本功能的关键领域之一。在这项工作中，通过基于数据驱动的ML框架对初始触发后的预测级联大小进行建模和分析。在级联的不同阶段，对随机森林、决策树、k近邻和人工神经网络等多种最大似然预测技术的预测性能进行了评估。已经观察到，叶栅早期阶段的潮流数据足以在预测大型叶栅的风险方面取得令人满意的性能。
Cascading failures, although not very common, are remaining concerns in power systems, which can result in enormous electricity service interruption with massive costs for society. Extensive research has been conducted to understand these complex phenomena and mitigate their effects. With the availability of large energy data, data-driven and machine learning (ML)-based techniques have emerged to support the efforts towards cascade resilient power systems. Predicting the risk and scale of cascading failures is one of the key areas that can support other essential functions for controlling and mitigating such events. In this work, predicting cascade size after the initial triggers are modeled and analyzed through data-driven ML-based frameworks. The prediction performance of various ML techniques including Random Forest, Decision Tree, k-Nearest Neighbor, and Artificial Neural Network are evaluated at different stages of the cascade. It has been observed that the power flow data in the early stages of the cascade can be enough to achieve promising performance in predicting the risk of large cascades.