Small Signal Stability Analysis Using Fuzzy Controller and Artificial Neural Network Stabilizer

Power system Stabilizers are used in order to damp out the low frequency oscillations which are due to disturbances. This paper attempts to investigate the performance of Conventional Power System Stabilizer (CPSS), Fuzzy Logic Power System Stabilizer (FLPSS) and Artificial Neural Network based Power System Stabilizer (ANNPSS) under Prefault and Postfault conditions for different loadings. The parameters of Conventional Power System Stabilizer (CPSS) is designed using Pole-Placement Technique and the parameters of Fuzzy logic Power System Stabilizer (FLPSS) is tuned to their optimal values in order to minimize the overshoot in step response of rotor angle International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print) ISSN 0976 – 6553(Online), Volume 1 Number 1, May June (2010), pp. 47-70 © IAEME, http://www.iaeme.com/ijeet.html IJEET © I A E M E International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 1, Number 1, May June (2010), © IAEME 48 deviation using Particle Swarm Optimization (PSO) technique whereas Artificial Neural network based Power System Stabilizer (ANNPSS) is trained by Linear Optimal Control (LOC) theory. Designed power system stabilizers are applied to a single machine infinite bus system and are tested in four operational conditions; normal load, heavy load, light load and solid 3-phase fault occurrence in a transmission line. The simulation study reveals that the performance of Linear Optimal Control (LOC) based Artificial Neural Network Power System Stabilizer is much improved with Artificial Neural Network based Power System Stabilizer under different operating conditions.