Combination of rough set theory and artificial neural networks for transient stability assessment

2002 ◽  
Author(s):  
X.P. Gu ◽  
S.K. Tso ◽  
Q. Zhang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Set Theory ◽  
Rough Set ◽  
Transient Stability ◽  
Rough Set Theory ◽  
Stability Assessment ◽  
Artificial Neural

Applying Rough Set Theory to Establish Artificial Neural Networks Model for Short Term Incidence Rate Forecasting

2013 ◽  
Vol 347-350 ◽  
pp. 2937-2941 ◽  
Author(s):  
Xiang Yu Zhao ◽  
Liang Liang Ma
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Incidence Rate ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Forecast Model ◽  
Short Term ◽  
Artificial Neural ◽  
Input Variables

Choosing input variable and networks architecture are key processes for modeling short term incidence rate forecast by artificial neural networks, in this paper a method based on rough set theory is proposed to deal with them. In the proposed approach, the key factors that affect the incidence rate forecasting are firstly identified by rough set theory and then the input variables of forecast model can be determined. On the basis of the process mentioned above a set of influence rules can been obtained through reductive mining process of attributes and attribute values, then a neural networks of incidence rate forecast model is established on the rule set and BP-algorithm is adopt to optimize the networks. The method indicates that incidence rate forecast model can be established according some theoretical principles and avoiding blindness. A practical application is given at last to demonstrate the usefulness of the novel method.

Rules Mining of Power System Transient Stability Assessment Based on Clustering Analysis and Rough Set Theory

2014 ◽  
Vol 521 ◽  
pp. 418-422 ◽  
Author(s):  
Yan Xu ◽  
Xin Chen
Keyword(s):  
Power System ◽  
Set Theory ◽  
Rough Set ◽  
Clustering Analysis ◽  
Transient Stability ◽  
Rough Set Theory ◽  
Decision Table ◽  
Final Decision ◽  
Stability Assessment ◽  
Decision Attributes

Transient Stability Assessment (TSA) aims at assessing stability of power system operation state quickly. This paper introduces rough set theory and clustering analysis to assess power system transient stability. At first, the stability operation parameters and fault places are taken as feature attributes based on the trait of power system transient ability. K-means algorithm is used to make continuous attributes among feature attributes discrete. Then feature attributes and stability types are taken as conditional attributes and decision attributes respectively. Initial decision table is established. Finally, rough set theory is used to form final decision table and rules of TSA are obtained. The IEEE 9-Bus system is employed to demonstrate the validity of the proposed approach.

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