电力系统故障后定位、动态状态估计和最优量测配置的神经网络选择
Which Neural Network to Choose for Post-Fault Localization, Dynamic State Estimation and Optimal Measurement Placement in Power Systems?
摘要：
我们考虑一个电力传输系统监测相量测量单元（PMU）放置在重要的，但不是所有的，系统的节点。假设有足够数量的不同的单线故障，特别是故障前的状态和（未清除）故障后的状态，记录的PMU和可用于训练，我们，首先，设计一个全面的序列的神经网络（NN）定位故障线路。序列中不同的神经网络的性能，包括线性回归，前馈神经网络，AlexNet，图形卷积神经网络，神经线性常微分方程和神经图形为基础的常微分方程，根据所涉及的潮流物理的类型和数量排序，比较不同水平的可观测性。其次，我们建立了一系列先进的电力系统动态信息和神经元ODE基于机器学习计划的训练，给定故障前的状态，预测故障后的状态，也并行，估计系统参数。最后，第三，并继续与第一（故障定位）设置，我们设计了一个（基于神经网络）的算法，发现最佳PMU的位置。
英文摘要：
We consider a power transmission system monitored with Phasor Measurement Units (PMUs) placed at significant, but not all, nodes of the system. Assuming that a sufficient number of distinct single-line faults, specifically pre-fault state and (not cleared) post-fault state, are recorded by the PMUs and are available for training, we, first, design a comprehensive sequence of Neural Networks (NNs) locating the faulty line. Performance of different NNs in the sequence, including Linear Regression, Feed-Forward NN, AlexNet, Graphical Convolutional NN, Neural Linear ODE and Neural Graph-based ODE, ordered according to the type and amount of the power flow physics involved, are compared for different levels of observability. Second, we build a sequence of advanced Power-System-Dynamics-Informed and Neural-ODE based Machine Learning schemes trained, given pre-fault state, to predict the post-fault state and also, in parallel, to estimate system parameters. Finally, third, and continuing to work with the first (fault localization) setting we design a (NN-based) algorithm which discovers optimal PMU placement.
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