Learning Power System Dynamic Signatures using LSTM-Based Deep Neural Network: A Prototype Study on the New York State Grid
Learning Power System Dynamic Signatures using LSTM-Based Deep Neural Network: A Prototype Study on the New York State Grid
复制标题
使用基于 LSTM 的深度神经网络学习电力系统动态特征：纽约州电网的原型研究
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
B. Fardanesh
中科院分区：
文献类型：
--
作者：
Sayak Mukherjee;A. Darvishi;A. Chakrabortty;B. Fardanesh
在这篇文章中，我们提出了一个原型研究的分类动态事件使用深度学习(DL)工具为纽约州(NYS)电网。在PSS/E平台上，利用东互联的公用事业级全尺度输电规划模型生成用于学习的仿真数据。我们专门使用那些相量测量单元(PMU)安装在实际NYS电网中的公交位置的模拟数据，以加快事件检测和决策。我们首先使用一种无监督学习技术，即主成分分析(PCA)，对NYS网格进行一致性研究。然后，我们考虑了三种不同的动态分类场景。我们根据相干簇对发电损失情景进行分类，根据纽约独立系统运营商(NYISO)分区分隔对负荷损失进行分类，并定位沿中东部(CE)接口的输电线路损失。对于输电线路停运情况，我们通过所述界面改变电网应力水平，然后跳闸不同的主线，从而生成动态事件数据。这项研究使用的深度学习工具被称为长期短期记忆(LSTM)神经网络。这项工作旨在为下一代电网开发一个基于PMU的数据驱动的快速事故分析和决策体系结构。
In this paper we present a prototype study of classifying dynamic events using a deep learning (DL) tool for the New York State (NYS) power grid. We use the utility-level full-scale transmission planning model of Eastern Interconnection (EI) in the PSS/E platform for generating simulation data that are used for learning. We specifically use simulation data from those bus locations where Phasor Measurement Units (PMUs) are installed in the actual NYS grid for faster event detection and decision making. We first conduct the coherency study of the NYS grid using a unsupervised learning technique, namely Principal Component Analysis (PCA). Then we consider three different dynamic classification scenarios. We classify the loss of generation scenarios according to the coherent clusters, loss of loads according to the New York Independent System Operator (NYISO) zonal separation and localize the loss of transmission lines along the Central-East (CE) interface. For the transmission line outage scenario, we have generated dynamic event data by changing the grid stress level through the said interface and then tripping different major lines. The deep learning tool used for this study is known as the Long Short Term Memory (LSTM) neural networks. This work is intended toward developing a PMU-based data-driven fast contingency analysis and decision making architecture for the next generation power grid.