| Abstract: | It has been clarified 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 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 training data for the neural network. The neural network used is a multilayer type with a feedback from the output layer to the input layer. The trained neural network is examined by untrained operating conditions and faults. |
