Study on quick judgement of small signal stability using CNN. Issue 16 (26th November 2018)

• Record Type: Journal Article
• Authors: Shi, Dongyu; Yan, Jianfeng; Gao, Bo; Xie, Mei; Hou, Jinxiu; Li, Gang; Yu, Zhihong; Lv, Ying; Lu, Guangming
• Is Part Of: Journal of engineering. Volume 2019:Issue 16(2019)
• Page Start: 826
• Page End: 829
• Publication Date: 2018-11-26
• Subjects: neural nets -- stochastic processes -- load dispatching -- sampling methods -- power grids -- power system stability -- cellular neural nets -- power system security -- power engineering computing; CNN model -- signal stability metrics -- operation mode -- online analysis system -- quick judgement -- dynamic security assessment -- DSA -- operation systems -- time‐consuming calculation methods -- fast method -- designated oscillation mode -- example frequency -- online application -- stochastic neighbour embedding -- power system components -- convolutional neural network model; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2018.8839
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : 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.