Learning to infer: A new variational inference approach for power grid topology identification
Learning to infer: A new variational inference approach for power grid topology identification
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学习推理：一种用于电网拓扑识别的新变分推理方法
DOI：
10.1109/ssp.2016.7551827
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
H. Poor
中科院分区：
文献类型：
--
作者：
Yue Zhao;Jianshu Chen;H. Poor
电力网络的任意拓扑结构的识别是一个计算困难的问题，由于假设的数量，网络规模呈指数增长。提出了一种新的变分推理方法，用于对网络中的每条线路状态进行有效的边缘推理。变分模型的优化转化为判别学习问题，并作为判别学习问题求解。所开发的基于学习的方法的主要优点是，用于学习的标记数据可以以非常小的成本以任意大的量生成。因此，离线训练的力量被充分利用，以提供有效的实时拓扑识别。在IEEE 30节点系统中对所提出的方法进行了评估。该方法在变分模型相对简单且测量集不完备的情况下，对任意拓扑结构的电力网络都有很好的识别效果。
Identifying arbitrary topologies of power networks is a computationally hard problem due to the number of hypotheses that grows exponentially with the network size. A new variational inference approach is developed for efficient marginal inference of every line status in the network. Optimizing the variational model is transformed to and solved as a discriminative learning problem. A major advantage of the developed learning based approach is that the labeled data used for learning can be generated in an arbitrarily large amount at very little cost. As a result, the power of offline training is fully exploited to offer effective real-time topology identification. The proposed methods are evaluated in the IEEE 30-bus system. With relatively simple variational models and only an undercomplete measurement set, the proposed method already achieves very good performance in identifying arbitrary power network topologies.