DocumentCode :
2919508
Title :
A comparison between ANN based methods of critical clearing time estimation
Author :
Kucuktezcan, Cavit Fatih ; Genc, Veysel Murat Istemihan
Author_Institution :
Dept. of Electr. Eng., Istanbul Tech. Univ., Istanbul, Turkey
fYear :
2013
fDate :
28-30 Nov. 2013
Firstpage :
132
Lastpage :
136
Abstract :
This paper presents a methodology based on Artificial Neural Network (ANN) structures for the dynamic security assessment (DSA) of power systems. Proposed methodology involves, ANN approach for fast and accurate estimation of critical clearing time (CCT) values of credible faults occurring in the system, considering changes in the loading conditions and system topology. CCT is an important indicator that measures the transient stability of the system against critical contingencies. Offline trained ANNs can monitor online DSA without suffering from excessive computational burden of time domain simulations (TDS). Decision Trees are used as a feature selection tool to reduce the training time and ANN complexity, increasing the CCT estimation performance of the ANN applications studied in this work, Multi-Layer Perceptron, Radial Basis Neural Network, Generalized Regression Neural Network and Adaptive Neuro-Fuzzy Inference Systems. The proposed approach is applied to 16 generator-68 bus test system operating at various loading conditions and system topologies.
Keywords :
fuzzy neural nets; fuzzy reasoning; multilayer perceptrons; power engineering computing; power system faults; power system security; power system transient stability; radial basis function networks; regression analysis; 16 generator-68 bus test system; ANN approach; CCT value; DSA; TDS; adaptive neuro-fuzzy inference system; artificial neural network; critical clearing time estimation; decision tree; dynamic security assessment; feature selection tool; generalized regression neural network; multilayer perceptron; power system security; radial basis neural network; system topology; time domain simulation; transient stability measurement; Artificial neural networks; Estimation; Loading; Power system dynamics; Power system stability; Topology; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Electronics Engineering (ELECO), 2013 8th International Conference on
Conference_Location :
Bursa
Print_ISBN :
978-605-01-0504-9
Type :
conf
DOI :
10.1109/ELECO.2013.6713818
Filename :
6713818
Link To Document :
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