Supervisory Power System Stability Control using Neuro-fuzzy system and particle swarm optimization algorithm

This paper describes the design and implementation of advanced Supervisory Power System Stability Controller (SPSSC) using Neuro-fuzzy system, and MATLAB S-function tool where the controller is taught from data generated by simulating the system for the optimal control regime. The controller is compared to a multi-band control system which is utilized to stabilize the system for different operating conditions. Simulation results show that the supervisory power system stability controller has produced better control action in stabilizing the system for conditions such as: normal, after disturbance in the electrical national grid as a result of changing of the plant capacity like renewable energy units, high load reduction or in the worst case of fault in operating the system, e.g. phase short circuit to ground. The new controller led to making the settling time and overshoot after disturbances proved to be lower which means that the system can reach to stability in the shortest time and with minimum disruption. Such behaviour will improve the quality of the provided power to the power grid.