DocumentCode :
1274941
Title :
Rule extraction for voltage security margin estimation
Author :
Su, Mu-Chun ; Liu, Chih-Wen ; Chang, Chen-Sung
Author_Institution :
Dept. of Electr. Eng., Tamkang Univ., Tamsui, Taiwan
Volume :
46
Issue :
6
fYear :
1999
fDate :
12/1/1999 12:00:00 AM
Firstpage :
1114
Lastpage :
1122
Abstract :
Research efforts have been devoted to estimating voltage security margins which show how close the current operating point of a power system is to a voltage collapse point as assessment of voltage security. One main disadvantage of these techniques is that they require large computations, therefore, they are not efficient for on-line use in power control centers. In this paper, we propose a technique based on hyperrectangular composite neural networks (HRCNNs) and fuzzy hyperrectangular composite neural networks (FHRCNNs) for voltage security margin estimation. The technique provides us with much faster assessments of voltage security than conventional techniques. The two classes of HRCNNs and FHRCNNs integrate the paradigm of neural networks with the rule-based approach, rendering them more useful than either. The values of the network parameters, after sufficient training, can be utilized to generate crisp or fuzzy rules on the basis of preselected meaningful features. Extracted rules are helpful to explain the whole assessment procedure so the assessments are more capable of being trusted. In addition, the power system operators or corresponding experts can delete unimportant features or add some additional features to improve the performance and computational efficiency based on the evaluation of the extracted rules. The proposed technique was tested on 3000 simulated data randomly generated from operating conditions on the IEEE 30-bus system to indicate its high efficiency
Keywords :
fuzzy neural nets; knowledge based systems; power system analysis computing; power system security; power system stability; IEEE 30-bus system; computational efficiency improvement; crisp rules; fuzzy hyperrectangular composite neural networks; fuzzy rules; hyperrectangular composite neural networks; neural networks; power system current operating point; rule extraction; rule-based approach; training; voltage collapse point; voltage security assessment; voltage security margin estimation; Computational efficiency; Computational modeling; Data mining; Fuzzy neural networks; Neural networks; Power control; Power system security; Power system simulation; System testing; Voltage;
fLanguage :
English
Journal_Title :
Industrial Electronics, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0046
Type :
jour
DOI :
10.1109/41.807998
Filename :
807998
Link To Document :
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