A wide area measurement based neurocontrol for generation excitation systems

Power system is a highly interconnected nonlinear system that needs optimal and accurate control for continuous operation. Large power transfer through long transmission line between different electrical areas, stressed system and adverse interaction between local controllers, may give rise to slow frequency inter-area oscillations. The inter-area modes may not be visible from local measurements and hence it is useful to use remote measurement based centralized supplementary control. Wide area control systems (WACSs) using wide-area or global signals can provide remote auxiliary control to local controllers such as automatic voltage regulators, power system stabilizers, etc. to damp out inter-area oscillations. This paper presents a design and real time implementation of a nonlinear neural network based optimal wide area controller using adaptive critic design (ACD). The real time implementation of a power system model is carried out on a real time digital simulator (RTDS). The performance of the WACS as a power system stability agent is studied using a two-area power system under different operating conditions and contingencies. The WACS shows improvement in the damping of inter-area mode with the use of supplementary excitation control. In addition, results show that the designed controller can provide robust performance under small communication delay in remote signal transmission.