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首页> 外文期刊>International journal of electrical power and energy systems >A short-term voltage stability online prediction method based on graph convolutional networks and long short-term memory networks
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A short-term voltage stability online prediction method based on graph convolutional networks and long short-term memory networks

机译：基于图形卷积网络的短期电压稳定性在线预测方法和长短期内存网络

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摘要

Due to complex dynamic characteristics and large scale of power systems, it is a great challenge to predict short-term voltage stability (STVS) online. To address this challenge, a STVS online prediction method based on graph convolutional networks (GCN) and long short-term memory networks (LSTM) is proposed in this paper. Firstly, we propose a novel machine learning framework, GCN-LSTM, which is a combination of GCN and LSTM. Specifically, the GCN is used to capture spatial features of power grids, the LSTM is used to capture temporal features of power grids. Secondly, a STVS online prediction method based on the GCN-LSTM model is proposed. The proposed method can capture multiplex spatial-temporal STVS evolution trends and predict STVS results. Finally, case studies are carried out in two testing systems, including a modified 39-bus system and a 68-bus system. The training and testing data is generated by Power System Simulator / Engineering (PSS/E). Simulation results illustrate the high performance of the proposed method.
机译：由于具有复杂的动态特性和大规模的电力系统，预测在线短期电压稳定性（STV）是一个很大的挑战。为了解决这一挑战，本文提出了基于图形卷积网络（GCN）和长短期存储网络（LSTM）的STV在线预测方法。首先，我们提出了一种新颖的机器学习框架GCN-LSTM，它是GCN和LSTM的组合。具体地，GCN用于捕获电网的空间特征，LSTM用于捕获电网的时间特征。其次，提出了基于GCN-LSTM模型的STV在线预测方法。该方法可以捕获多路复用空间 - 时间STV演化趋势并预测STV结果。最后，在两个测试系统中进行了案例研究，包括修改的39总线系统和68总线系统。培训和测试数据由电力系统模拟器/工程（PSS / E）产生。仿真结果说明了所提出的方法的高性能。

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