Title :
Voltage stability security margin assessment via artificial neural networks
Author :
Jiménez, Alberto C. ; Castro, Carlos A.
Author_Institution :
Univ. of Campinas, Campinas
Abstract :
This paper presents an alternative approach to estimate voltage stability margins (VSM), both under normal operating conditions and contingencies, based on the use of one only multilayer artificial neural network (ANN). The ANN is trained using measurable quantities obtained directly from the system, or easily calculated ones obtained from load forecasted data. An input data set reduction procedure is also included, in order to guarantee the efficiency of the proposed method. Simulation results for the IEEE systems are shown to demonstrate the effectiveness of the proposed ANN.
Keywords :
neurocontrollers; power system control; power system protection; power system security; power system stability; voltage control; IEEE systems; multilayer artificial neural network; voltage stability security margin assessment; Artificial neural networks; Load forecasting; Multi-layer neural network; Neural networks; Power generation; Power system stability; Principal component analysis; Reactive power; Stability analysis; Voltage; Power systems stability; neural networks; voltage collapse prevention; voltage stability;
Conference_Titel :
Power Tech, 2005 IEEE Russia
Conference_Location :
St. Petersburg
Print_ISBN :
978-5-93208-034-4
Electronic_ISBN :
978-5-93208-034-4
DOI :
10.1109/PTC.2005.4524649