Open AccessStudy on quick judgement of small signal stability using CNN
Author(s) -
Shi Dongyu,
Yan Jianfeng,
Gao Bo,
Xie Mei,
Hou Jinxiu,
Li Gang,
Yu Zhihong,
Lv Ying,
Lu Guangming
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8839
Subject(s) - computer science , stability (learning theory) , signal (programming language) , convolutional neural network , mode (computer interface) , state (computer science) , electric power system , algorithm , grid , power (physics) , artificial intelligence , machine learning , mathematics , physics , geometry , quantum mechanics , programming language , operating system
Dynamic security assessment (DSA) is widely used in dispatching operation systems, and the small signal stability is one of the DSA's most time‐consuming calculation methods. In this article, a fast method is proposed aiming to predict the small signal stability metrics of designated oscillation mode, for example frequency or damping ratio. The method is much faster than the simulation and suitable for the online application. First, the t ‐distributed stochastic neighbour embedding ( t ‐SNE) algorithm is performed which can create a mapping from the power system components to 2D coordinate depending on the electrical distance of each other; then, it will be transformed into a grid structure by meshing operation, on which the convolutional neural network (CNN) model can be run properly. Finally, with a large amount of simulation samples, the CNN model can be well trained using static quantities as its input and small signal stability metrics as its prediction target. While a new operation mode needs to be evaluated, the result will be obtained by CNN directly. The validity of proposed method is verified using online data of State Grid Corp of China. It is proved that the method meets the requirements for speed and accuracy of online analysis system.