Efficient neural network architecture for topology identification in smart grid
Efficient neural network architecture for topology identification in smart grid
复制标题
用于智能电网拓扑识别的高效神经网络架构
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
H. Poor
中科院分区：
文献类型：
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作者：
Yue Zhao;Jianshu Chen;H. Poor
研究了基于电网测量数据的任意电网拓扑真实的实时辨识问题。一种基于学习的方法是：训练二进制分类器近似最大后验概率（MAP）检测器，每个识别不同线路的状态。开发了一种高效的神经网络结构，其中所有线路状态的推理功能共享。该架构在训练和测试过程中具有显著的计算复杂度优势。在IEEE 30节点系统中对基于神经网络的分类器进行了评估。它表明，使用所提出的特征共享神经网络架构，a）的训练和测试时间大大减少相比，训练一个单独的神经网络为每个线路状态推理，和B）少量的训练数据是足够的，以实现一个非常好的实时拓扑识别性能。
Identifying arbitrary power grid topologies in real time based on measurements in the grid is studied. A learning based approach is developed: binary classifiers are trained to approximate the maximum a-posteriori probability (MAP) detectors that each identifies the status of a distinct line. An efficient neural network architecture in which features are shared for inferences of all line statuses is developed. This architecture enjoys a significant computational complexity advantage in the training and testing processes. The developed classifiers based on neural networks are evaluated in the IEEE 30-bus system. It is demonstrated that, using the proposed feature sharing neural network architecture, a) the training and testing times are drastically reduced compared with training a separate neural network for each line status inference, and b) a small amount of training data is sufficient for achieving a very good real-time topology identification performance.