Prediction of Disturbed Trajectories in Transient Stability Analysis Based on Self-memory Gray Verhulst Model

被引：0
|
作者：
Huang D. [1 ]
Yang X. [2 ]
Chen S. [1 ,3 ]
机构：
[1] School of Electrical Engineering, Beijing Jiaotong University, Beijing
[2] School of Automation, Beijing Information Science & Technology University, Beijing
[3] China Electric Power Research Institute, Beijing
来源：
Gaodianya Jishu/High Voltage Engineering | 2018年 / 44卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Disturbed trajectory prediction; Gray Verhulst model; Prediction performance; Self-memory prediction; Wide-area measurement system;
D O I：
10.13336/j.1003-6520.hve.20180329031
中图分类号：
学科分类号：
摘要：
WAMS (wide area measurement system) can monitor real-time trajectories of power system on unified time scale which makes it possible to analyze the real-time transient stability. Based on the gray system theory and self-memory principle of dynamical system,we proposed a method for the disturbed rotor angle trajectory prediction based on gray Verhulst self-memory model. The trend of disturbed rotor angle trajectory during every swing conforms the sigmoid process of gray Verhulst model. For high randomness and nonlinearity of disturbed rotor angle trajectory, the self-memory gray Verhulst model is built based on the traditional gray Verhulst model prediction. Since the prediction not only considers the change trend of disturbed rotor angle trajectory but also uses the historical data, the random fluctuation of disturbed rotor angle trajectory can be characterized well and the prediction performance is improved. Calculation results of New England 10-machine 30-bus system show that the proposed prediction method is effective and can offer technical supports for real-time transient stability analysis. © 2018, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
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页码：1285 / 1291
页数：6
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