电力系统自动化2019，Vol.43Issue(2)：50-57,8.DOI:10.7500/AEPS20171203004
基于卷积神经网络的电力系统小干扰稳定评估
Small-signal Stability Assessment of Power System Based on Convolutional Neural Network
摘要
Abstract
As the scale of power system increases, traditional numerical methods in eigenvalue computation for small-signal stability assessment are unable to meet the requirement of real-time analysis.Therefore, this paper proposes a small-signal stability assessment method based on deep-learning (convolutional neural network). This method takes the signals of wide-area measurement system as inputs of the model and generates critical eigenvalues as outputs.After the necessary preprocessing of the inputs and outputs, the mapping relationship between inputs and outputs can be established by deep neural network.Discrete cosine transformation and graphics processing unit parallelization techniques are employed to overcome the challenges from high dimension and slow training rate of large-scale system.Case study results indicate that the proposed method is able to accurately obtain the critical eigenvalues of the studied system after offline training using historic data, given no significant change in control parameters.关键词
卷积神经网络/深度学习/小干扰稳定评估/关键特征值/广域测量系统Key words
convolutional neural network/deep learning/small-signal stability assessment/key eigenvalues/wide-area measurement system引用本文复制引用
李洋麟,江全元,颜融,耿光超..基于卷积神经网络的电力系统小干扰稳定评估[J].电力系统自动化,2019,43(2):50-57,8.基金项目
国家自然科学基金资助项目 (51677164).This work is supported by National Natural Science Foundation of China (No. 51677164). （51677164）
