The paper presents an artificial neural network (ANN) based Proportional Integral Derivative
(PID) controller for a superconducting generator (SCG) in a multi-machine power system. The
studied power system includes a SCG and three conventional machines of different types and
ratings. The SCG is controlled also by conventional PID, which is designed according to pole
placement technique, implemented in its governor control loop. While, conventional generating
units are controlled by different conventional excitation control systems. The ANN controller
patterns are gathered from a simple unsupervised (self learning) ANN-PID using an optimization
technique. To achieve a high degree of accuracy, the system is represented by a fairly detailed
non-linear model. The simulation results reveal that, the proposed ANN controller is achieving
further enhancement of the system performance in terms of damping increase and fast return of
system variables to their nominal values over a wide range of operating conditions and under sever
disturbances such as 3-phase short circuit, step increase in load, and 3-phase short circuit followed
by one line outage compared to conventional PID controller