Power System Stabilizing Control by Superconducting Magnetic Energy Storage Using Neural Network

Abstract

It has been made clear that a superconducting magnetic energy storage (SMES) is very effective for power system stabilization. The control methods proposed for power system stabilization by SMES are such as the pole assignment, the optimal control and so on, each of which, however, has its drawbacks.
This paper is concerned with the power system stabilization by neural network control of the active power of SMES. First, the optimal stabilizing control of the SMES power for the model power system is calculated for various power system operating conditions and fault conditions. Then, these optimal controls are used as the training data for the neural network. The neural network used is a multi-layer type with a feedback from the output layer to the input layer. The trained neural network is examined by untrained operating conditions and faults.