Neural networks for adaptive control coordination of PSSs and FACTS devices in multimachine power system

Abstract

The paper develops a new design procedure for online control coordination which leads to adaptive power system stabilisers (PSSs) and/or supplementary damping controllers of flexible ac transmission system (FACTS) devices for enhancing the stability of the electromechanical modes in a multimachine power system. The controller parameters are adaptive to the changes in system operating condition and/or configuration. Central to the design is the use of a neural network synthesised to give in its output layer the optimal controller parameters adaptive to system operating condition and configuration. A novel feature of the neural-adaptive controller is that of representing the system configuration by a reduced nodal impedance matrix which is input to the neural network. Only power network nodes with direct connections to generators and FACTS devices are retained in the reduced nodal impedance matrix. The system operating condition is represented in terms of the measured generator power loadings, which are also input to the neural network. For a representative power system, the neural network is trained and tested for a wide range of credible operating conditions and contingencies. Both eigenvalue calculations and time–domain simulations are used in the testing and verification of the dynamic performance of the neural-adaptive controller.