基于深度学习的电力系统暂态稳定评估方法

Transient Stability Assessment of Power System Based on Deep Learning Technology

在机器学习领域,暂态稳定评估问题被定义为通过大量故障样本来估计稳定边界的二分类问题。该文提出了一种深度学习方法来解决这个二分类问题。该方法包含4个步骤:首先,利用样本数据构建原始输入特征来描述电力系统动态特性;然后,采用变分自动编码器(variational auto-encoders,VAE)对原始输入特征进行无监督学习实现特征抽取,从而获得高阶特征;之后,对卷积神经网络(convolution neural network,CNN)进行有监督学习训练得到高阶特征与电力系统暂态稳定性之间的映射关系;最后,将训练得到的模型应用于电力系统在线暂态稳定评估。在新英格兰39节点测试电力系统的仿真试验表明,所提出的暂态稳定评估(transient stability assessment,TSA)模型具有评估精度高、不稳定样本评估错误率低、抗噪声干扰能力强的特点,适合基于广域测量信息的准实时在线暂态稳定评估。

In the field of machine learning,transient stability assessment can be considered as a two-class problem of estimating the stability boundary through large number of fault samples. This paper proposes a method of deep learning to solve this problem. The method consists of four stages： firstly,using samples to construct the original input feature for describing the dynamic characteristics of the power system; secondly,variational auto-encoders（ VAE） is used to perform unsupervised learning on the original input feature to obtain high-order features; thirdly, the supervised training of convolution neural network（ CNN） is carried out to obtain the relationship between high order characteristic and transient stability of power system; finally,the model is applied to the transient stability assessment of power system. Simulation on the NewEngland 39-bus test system shows that the proposed approach has high accuracy,rare misclassification of unstable sample and excellent robustness with noise for transient stability assessment（ TSA）. Therefore,it is suitable for quasi-realtime online transient stability assessment based on wide-area measurement information.