Title :
Hybrid GA-SVR technique for contingency screening in power system
Author :
Kumar, Ajit ; Tripathi, Priyanka
Author_Institution :
Dept. of Power Technol., INCRC ABB Global Ind. Solution Ltd., Bangalore, India
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.
Keywords :
genetic algorithms; power system analysis computing; regression analysis; support vector machines; IEEE-30 bus system; fast contingency analysis method; global optimal parameters searching; hybrid GA-SVR technique; hybrid genetic algorithm; load profile; network topology; online contingency screening method; power system; support vector regression technique; Artificial neural networks; Genetic algorithms; Load flow; Support vector machines; Testing; Training; Active and Reactive Power Performance Indices; Contingency Screening; Genetic Algorithm; Support Vector Regression;
Conference_Titel :
Power India Conference, 2012 IEEE Fifth
Conference_Location :
Murthal
Print_ISBN :
978-1-4673-0763-5
DOI :
10.1109/PowerI.2012.6479476
