Improvement of Small Signal Stability of SMIB System with Optimized Power System Stabilizer
Y. Raghuvamsi1, Imran Abdul2, Ch. Rajesh3

1Mr. Y. Raghuvamsi*, EEE, Lakireddy Bali Reddy College of Engineering (A), Mylavaram, A.P., India.
2Mr. Imran Abdul, EEE, Lakireddy Bali Reddy College of Engineering (A), Mylavaram, A.P., India.
3Mr. Ch. Rajesh, EEE, Lakireddy Bali Reddy College of Engineering (A), Mylavaram, A.P., India.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 29, 2020. | Manuscript published on May 10, 2020. | PP: 709-716 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5756059720/2020©BEIESP | DOI: 10.35940/ijitee.G5756.059720
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Abstract: As electrical power system is a complex system, there are more chances of stability issues may arise. One of the stability issues is Low Frequency Oscillations (LFOs) which makes the system unstable. As these oscillations are having low frequency i.e. large time constant with slowly increasing magnitude, they are referred to small signal stability. The main reason of these oscillations is due to lack of sufficient damping torque. Automatic Voltage Regulator (AVR) action in generator is providing sufficient synchronizing torque for system stability. This is possible with high gain and low time constant AVR which results in reduction of damping torque. Power System Stabilizer (PSS) is used together with AVR for providing necessary damping torque to minimize the LFOs. For effective damping, the PSS performance is improved by optimizing its parameters. In this paper, Single Machine Infinite Bus (SMIB) system is considered for studying the effect of LFOs. The SMIB system is simulated for a step disturbance in reference voltage and the results are carried out for different optimizing techniques Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO), Teaching and Learning based Optimization (TLBO). 
Keywords:  Automatic Voltage Regulator, Cat Swarm Optimization, Particle Swarm Optimization, Power System Stabilizer, Teaching and Learning based Optimization.
Scope of the Article: Swarm Intelligence