Power System Transient Stability Rules Extraction Based on Multi-Attribute Decision Tree

被引:0
|
作者
Shi F. [1 ]
Zhang L. [1 ]
Hu X. [2 ]
Yu Z. [3 ]
Zhang H. [1 ]
机构
[1] The Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education, Shandong University, Ji'nan
[2] State Grid Jiangxi Electric Power Co. Ltd, Nanchang Power Supply Branch, Nanchang
[3] China Electric Power Research Institute, Beijing
来源
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2019年 / 34卷 / 11期
关键词
Association rules; Decision tree; Linear discriminant analysis; Mutual information; Transient stability margin;
D O I
10.19595/j.cnki.1000-6753.tces.180648
中图分类号
学科分类号
摘要
With the development of the renewable energy generation and the AC-DC hybrid interconnected power grid, the operation and dispatching mode of the power grid is more complex and changeable. The traditional dispatch and control method based on online computer-assisted decision support and artificial experience is no longer suitable for the up-to-date power grid. In this paper, a multi-attribute decision tree is proposed based on transient stability margin index. The mutual information is used to primarily analyze the electrical variables. Then linear discriminant analysis is applied to get the best the projection of the selected attributes, from which the combination feature of attributes can be get. The decision tree is constructed after the discretization of the transient stability margins under some specified faults, then the general rules for evaluating the stability of the system are achieved. The operation mode adjustment strategies for improving the system stability are obtained through the backward analysis of the decision tree. Finally, the IEEE-39 node test system is used to verify the correctness and effectiveness of the proposed method. © 2019, Electrical Technology Press Co. Ltd. All right reserved.
引用
收藏
页码:2364 / 2374
页数:10
相关论文
共 27 条
  • [11] Shyh-Jier H., Jeu-Min L., Enhancement of anomalous data mining in power system predicting-aided state estimation, IEEE Transactions on Power Systems, 19, 1, pp. 610-619, (2004)
  • [12] Yao D., Jia H., Zhao S., Power system transient stability assessment and stability margin predic-tion base on compound neural network, Automation of Electric Power Systems, 37, 20, pp. 41-46, (2013)
  • [13] Ye S., Wang X., Liu Z., Et al., Power system transient stability assessment base on support vector machine incremental learning method, Automation of Electric Power Systems, 35, 11, pp. 15-19, (2011)
  • [14] Zheng C., Malbasa V., Kezunovic M., Regression tree for stability margin prediction using synchrophasor meas-urements, IEEE Transactions on Power Systems, 28, 2, pp. 1978-1987, (2013)
  • [15] Rovnyak S., Kretsinger S., Thorp J., Et al., Decision trees for real-time transient stability prediction, IEEE Transactions on Power Systems, 9, 3, pp. 1417-1426, (1994)
  • [16] Diao R., Vittal V., Logic N., Design of a real-time security assessment tool for situational awareness enhancement in modern power systems, IEEE Transactions on Power Systems, 25, 2, pp. 957-965, (2010)
  • [17] Wang K., Sun H., Zhang B., Et al., Transient stability assessment based on 2D combined at-tribute decision tree, Proceedings of the CSEE, 29, pp. 17-24, (2009)
  • [18] Xu Y., Dong Z., Zhang R., Et al., A decision tree-based on-line preventive control strategy for power system transient instability prevention, International Journal of Systems Science, 45, 2, pp. 176-186, (2014)
  • [19] Genc I., Diao R., Vittal V., Et al., Decision tree-based preventive and corrective control applications for dynamic security enhancement in power systems, IEEE Transactions on Power Systems, 25, 3, pp. 1611-1619, (2010)
  • [20] Gao Q., Rovnyak S.M., Decision trees using synchronized phasor measurements for wide-area response-based control, IEEE Transactions on Power Systems, 26, 2, pp. 855-861, (2011)