A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification
A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification
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DOI：
10.1109/tsg.2019.2925405
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发表时间：
2017-10
影响因子：
9.6
通讯作者：
Yue Zhao;Jianshu Chen;H. Poor
中科院分区：
文献类型：
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作者：
Yue Zhao;Jianshu Chen;H. Poor
在真实的时间内识别输电网络中潜在的大量同时线路停电是一个计算困难的问题。这是因为假设的数量随着网络规模呈指数增长。提出了一种新的“学习推理”方法，用于有效地推理网络中的每条线路的状态。优化线路故障检测器转化为判别式学习问题，并解决了基于蒙特卡罗样本产生的潮流模拟。所开发的学习推断方法的一个主要优点是，用于训练的标记数据可以以任意大的数量快速生成，并且成本很低。因此，离线训练的力量被充分利用来学习非常复杂的分类器，以实现有效的实时多线路停电识别。在IEEE 30，118和300总线系统中评估所提出的方法。在真实的时间内识别多线路停电的优异性能是用合理的少量数据实现的。
Identifying a potentially large number of simultaneous line outages in power transmission networks in real time is a computationally hard problem. This is because the number of hypotheses grows exponentially with the network size. A new “Learning-to-Infer” method is developed for efficient inference of every line status in the network. Optimizing the line outage detector is transformed to and solved as a discriminative learning problem based on Monte Carlo samples generated with power flow simulations. A major advantage of the developed Learning-to-Infer method is that the labeled data used for training can be generated in an arbitrarily large amount rapidly and at very little cost. As a result, the power of offline training is fully exploited to learn very complex classifiers for effective real-time multi-line outage identification. The proposed methods are evaluated in the IEEE 30, 118, and 300 bus systems. Excellent performance in identifying multi-line outages in real time is achieved with a reasonably small amount of data.