Title :
Artificial neural network application to alleviate voltage instability problem
Author :
Sallam, A.A. ; Khafaga, A.M.
Author_Institution :
Dept. of Electr. Eng., Suez Canal Univ., Port Said, Egypt
Abstract :
This paper argues that the artificial neural network (ANN) is an important technique and can be applied to control voltage instability problems in two ways. The first is to control voltage collapse by using load shedding. In this way the minimum and optimal ratio of load shedding can be calculated. The second is to calculate the reactive power required to control sources in the electric power system. The strengths of this powerful technique lie in its ability for modeling and solving many types of problems. The ANN is designed for those two mentioned ways. A multi-layer feed forward ANN trained with error backpropagation learning is proposed. This network is applied to a stressed power system at different load levels. Simulation results on a test system are reported in this paper.
Keywords :
backpropagation; feedforward neural nets; load shedding; multilayer perceptrons; neurocontrollers; power system control; power system dynamic stability; reactive power control; voltage control; ANN training; approximate reasoning; artificial neural network; error backpropagation learning; load management; load shedding; multi-layers feed forward ANN; reactive power; reactive power control; voltage collapse control; voltage instability control; Artificial neural networks; Control systems; Load flow; Power system modeling; Power system protection; Power system simulation; Power system stability; Reactive power; Reactive power control; Voltage control;
Conference_Titel :
Power Engineering 2002 Large Engineering Systems Conference on, LESCOPE 02
Print_ISBN :
0-7803-7520-3
DOI :
10.1109/LESCPE.2002.1020679