Title :
A neural network control approach to voltage stability enhancement
Author :
Rajalakshmi, P. ; Rathinakumar, M.
Author_Institution :
Dept. of Electr. & Electron. Eng., SCSVMV Univ., Kanchipuram, India
Abstract :
In this paper the static voltage stability index has been identified by minimum singular value of the power flow Jacobian matrix. The system security in power system models by the scalar magnitude of a voltage stability index may be a very difficult task. When we are taking account of reactive power generation limits it is very difficult to predict stability index at voltage collapse points. Here a quick method is used to calculate the minimum singular value and the corresponding left and right singular vectors are presented. This developed algorithm is very useful in online because it required small amount of computation time. For different patterns of load and generation are increases, to determine voltage collapse point. For solving the maximum loading problem an optimal power flow approach was used with these points the MSV is calculated and used for training and testing the Neural Network.
Keywords :
Jacobian matrices; load flow; neurocontrollers; power system dynamic stability; voltage control; MSV; maximum loading problem; minimum singular value; neural network; neural network control approach; optimal power flow approach; power flow Jacobian matrix; reactive power generation limits; static voltage stability index; voltage collapse points; voltage stability enhancement; Jacobian matrices; Load flow; Matrix decomposition; Power system stability; Reactive power; Stability criteria; FACTS; IEEE 30 Bus system; MATLAB; Minimum singular value; Neural network; Participation factor; Voltage collapse;
Conference_Titel :
Emerging Trends In New & Renewable Energy Sources And Energy Management (NCET NRES EM), 2014 IEEE National Conference On
Print_ISBN :
978-1-4799-8193-9
DOI :
10.1109/NCETNRESEM.2014.7088734