NDLI: Hybrid GA-SVR technique for contingency screening in power system

Content Provider	IEEE Xplore Digital Library
Author	Kumar, A. Tripathi, P.
Copyright Year	2012
Description	Author affiliation: Department of Electrical Engineering Indian Institute of Technology Roorkee, Roorkee, India (Tripathi, P.) \|\| Department of Power Technology, INCRC ABB Global Industrial Solution Ltd Bangalore, India (Kumar, A.)
Abstract	Contingency analysis is essential to operate power system in secure state. Normally Contingency analysis is performed offline since it is a time consuming task. Due to continuously changing nature of power system it is imperative to have a fast contingency analysis method. This work proposed a very efficient, fast and accurate method for online contingency screening using the hybrid Genetic Algorithm (GA) and Support Vector Regression (SVR), hence named GA-SVR. SVR is used to build a model which takes a network topology and load profile as input and maps them into contingency performance indices. It is desirable to find the optimal value of SVR parameters to make it fast and accurate. Therefore GA is applied for searching global optimal parameters for SVR model. Standard IEEE-30 bus system is tested to illustrate the effectiveness of presented GA-SVR technique.
Starting Page	1
Ending Page	6
File Size	679147
Page Count	6
File Format	PDF
ISBN	9781467307635
e-ISBN	9781467307666
DOI	10.1109/PowerI.2012.6479476
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-19
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Contingency Screening Artificial neural networks Support Vector Regression Genetic Algorithm Active and Reactive Power Performance Indices Genetic algorithms Testing Load flow
Content Type	Text
Resource Type	Article

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