Using Neural Networks to Detect Line Outages from PMU Data
Using Neural Networks to Detect Line Outages from PMU Data
复制标题
使用神经网络从 PMU 数据检测线路中断
DOI：
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发表时间：
2017
期刊：
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通讯作者：
Stephen J. Wright
中科院分区：
文献类型：
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作者：
Ching;Stephen J. Wright
我们提出了一种基于神经网络和交流潮流方程的方法，利用相量测量单元传感器 (PMU) 的信息来识别电网中的单线和双线断电。我们的方法不是通过反转物理模型来从传感器数据推断中断，而是使用 AC 模型来模拟传感器在多种需求和季节性条件下对所有感兴趣的中断的响应，并使用结果数据来训练神经网络分类器，以直接从传感器数据识别和区分不同的中断事件。训练后，分类器的实时部署只需要一些矩阵向量乘积和简单的向量运算。这些操作的执行速度比基于交流功率流的模型反演要快得多，交流功率流由非线性方程和可能代表线路停电的整数/二进制变量以及代表电压和功率流的变量组成。我们之所以使用神经网络，是因为它在计算机视觉和自然语言处理等领域的成功应用。神经网络自动查找原始数据的非线性变换，突出显示使分类任务更容易的有用特征。我们进一步考虑对仅放置在公交车子集的传感器的中断进行分类的问题，描述了选择传感器位置的原则方法，并表明可以通过一组有限的测量来实现高度准确的分类。
We propose an approach based on neural networks and the AC power flow equations to identify single- and double- line outages in a power grid using the information from phasor measurement unit sensors (PMUs). Rather than inferring the outage from the sensor data by inverting the physical model, our approach uses the AC model to simulate sensor responses to all outages of interest under multiple demand and seasonal conditions, and uses the resulting data to train a neural network classifier to recognize and discriminate between different outage events directly from sensor data. After training, real-time deployment of the classifier requires just a few matrix-vector products and simple vector operations. These operations can be executed much more rapidly than inversion of a model based on AC power flow, which consists of nonlinear equations and possibly integer / binary variables representing line outages, as well as the variables representing voltages and power flows. We are motivated to use neural network by its successful application to such areas as computer vision and natural language processing. Neural networks automatically find nonlinear transformations of the raw data that highlight useful features that make the classification task easier. We further consider the problem of classifying outages from sensors placed at only a subset of the buses, describing a principled way to choose sensor locations and showing that highly accurate classification can be achieved from a restricted set of measurements.