Power system transient stability assessment based on cluster features of rotor angle trajectories

被引：0
|
作者：
Zhou Y. [1 ]
Wu J. [1 ]
Yu Z. [2 ]
Ji L. [1 ]
Yan J. [2 ]
Hao L. [1 ]
机构：
[1] School of Electrical Engineering, Beijing Jiaotong University, Haidian District, Beijing
[2] China Electric Power Research Institute, Haidian District, Beijing
来源：
| 1600年 / Power System Technology Press卷 / 40期
关键词：
Decision tree; Machine learning; Rotor angle trajectories; Support vector machine; Transient stability assessment;
D O I：
10.13335/j.1000-3673.pst.2016.05.028
中图分类号：
学科分类号：
摘要：
Multiple machine learning techniques have been widely used in transient stability analysis. For machine learning based method, balance between input feature number and total calculation efficiency is always a problem need to solve. In this paper, a hybrid classifier combining linear support vector machine (LSVM) and decision tree (DT)was proposed to assess transient stability using rotor angle trajectory cluster features. Firstly, rotor angle cluster features were used as inputs. Considering time dimension of input features, each time series feature was reduced with LSVM. Then the reduced data were put into DT to generate transient stability prediction and stability degree evaluation models. Boosting technique was used to improve accuracy of the evaluation model. Case studies were conducted on New England 10-machine 39-bus system to verify the proposed method. Test results showed that the proposed cluster features and algorithm possesses high accuracy and overall calculation efficiency. The evaluation model could indicate stability degree accurately and was robust to untrained samples. © 2016, Power System Technology Press. All right reserved.
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页码：1482 / 1487
页数：5
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