DocumentCode :
2275166
Title :
A novel approach for improving voltage stability margin by sensitivity analysis of Neural Network
Author :
Aghamohammadi, M.R. ; Hashemi, S. ; Ghazizadeh, M.S.
Author_Institution :
Dept. of Electr. Eng., Power & Water Univ. of Technol., Tehran, Iran
fYear :
2010
fDate :
27-29 Oct. 2010
Firstpage :
280
Lastpage :
286
Abstract :
This paper presents a new approach for estimating and improving voltage stability margin from phase and magnitude profiles of bus voltages using sensitivity analysis of Voltage Stability Assessment Neural Network (VSANN). Voltage profile contains useful information about system stability margin including the effect of load-generation pattern, line outage and reactive power compensation, so it is adopted as the input pattern of VSANN. In fact, VSANN approximates the functional relationship between VSM and the voltage profile. The sensitivity analysis of VSM with respect to reactive power compensation extracted from information stored in the weighting factor of VSANN is the most dominant feature of the proposed approach. Sensitivity of VSM helps one to select the most effective buses for reactive power compensation aimed to enhance VSM. The proposed approach has been implemented in IEEE 39-bus test system with promising results showing its effectiveness and applicability.
Keywords :
neural nets; power engineering computing; power system security; power system stability; reactive power; sensitivity analysis; IEEE 39-bus test system; VSANN; VSM; bus voltages; line outage; load-generation pattern; power system security; reactive power compensation; sensitivity analysis; voltage stability assessment neural network; voltage stability margin; Feature Extraction; Neural Networks; Sensitivity Analysis; Voltage security margin;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
IPEC, 2010 Conference Proceedings
Conference_Location :
Singapore
ISSN :
1947-1262
Print_ISBN :
978-1-4244-7399-1
Type :
conf
DOI :
10.1109/IPECON.2010.5697145
Filename :
5697145
Link To Document :
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