Design of robust modified power system stabilizer for dynamic stability improvement using Particle Swarm Optimization technique

Abstract This paper presents a novel method for designing robust Power System Stabilizer (PSS) using Particle Swarm Optimization (PSO) technique to improve the dynamic stability of the power system. Modified Heffron-Phillip’s model is developed by considering the generator side transformer voltage as the reference instead of considering the infinite bus voltage as the reference to reduce the complexity and computational time. Using this MHP model, a power system stabilizer called Modified Power System Stabilizer (MPSS) is developed and integrated with P-I-D controller using PSO (PSO-P-I-D-MPSS) on a Single Machine Infinite Bus System. PSO algorithm is used to determine the gain settings of the P-I-D-MPSS. The developed PSO-P-I-D-MPSS is simple to implement and will be a better alternative to the conventional or evolutionary based PSSs. The efficacy of the proposed PSO-P-I-D-MPSS is validated by the application of proposed stabilizer to a benchmark system for several operating conditions under various disturbances. The results clearly show that the proposed stabilizer improves the dynamic stability of the power system under wide range of operating conditions.

[1]  Dong Hwa Kim Hybrid GA-BF based intelligent PID controller tuning for AVR system , 2011, Appl. Soft Comput..

[2]  K. R. Padiyar,et al.  Power system dynamics : stability and control , 1996 .

[3]  Vijay Kumar Tayal,et al.  Reduced order H∞ TCSC controller & PSO optimized fuzzy PSS design in mitigating small signal oscillations in a wide range , 2015 .

[4]  Charles Concordia,et al.  Concepts of Synchronous Machine Stability as Affected by Excitation Control , 1969 .

[5]  P.K. Dhal Dynamic stability analysis by selection of optimal location of STATCOM through power system stabilizer tuned by particle swarm optimization technique , 2017, 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS).

[6]  Leticia Amador-Angulo,et al.  A new fuzzy bee colony optimization with dynamic adaptation of parameters using interval type-2 fuzzy logic for tuning fuzzy controllers , 2016, Soft Computing.

[7]  I. Sen,et al.  Synchronizing and Damping Torques Analysis of Nonlinear Voltage Regulators , 2011, IEEE Transactions on Power Systems.

[8]  Hossam E.A. Talaat,et al.  Design and experimental investigation of a decentralized GA-optimized neuro-fuzzy power system stabilizer , 2010 .

[9]  Gurunath Gurrala,et al.  Design of Pole Placement Power System Stabilizers for Multi-Machine Systems without the External System Information , 2013 .

[10]  M. A. Abido,et al.  A novel approach to conventional power system stabilizer design using tabu search , 1999 .

[11]  A. Chatterjee,et al.  Chaotic ant swarm optimization for fuzzy-based tuning of power system stabilizer , 2011 .

[12]  Graham Rogers,et al.  Power System Oscillations , 1999 .

[13]  Radhakant Padhi,et al.  Single network adaptive critic design for power system stabilisers , 2009 .

[14]  A. Chatterjee,et al.  Bio-inspired fuzzy logic based tuning of power system stabilizer , 2009, Expert Syst. Appl..

[15]  Jeevamma Jacob,et al.  Fractional-order lead-lag compensator-based multi-band power system stabiliser design using a hybrid dynamic GA-PSO algorithm , 2018 .

[16]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[17]  K. P. Wong,et al.  Robust power system stabiliser design under multi-operating conditions using differential evolution , 2008 .

[18]  Jitendriya Ku Satapathy,et al.  Time delay approach for PSS and SSSC based coordinated controller design using hybrid PSO–GSA algorithm , 2015 .

[19]  O.P. Malik,et al.  Neurofuzzy Power System Stabilizer , 2008, IEEE Transactions on Energy Conversion.

[20]  M. A. Abido,et al.  Simultaneous stabilization of multimachine power systems via genetic algorithms , 1999, IEEE Transactions on Power Systems.

[21]  Oscar Castillo,et al.  Dynamic parameter adaptation in particle swarm optimization using interval type-2 fuzzy logic , 2014, Soft Computing.

[22]  Oscar Castillo,et al.  A hybrid optimization method with PSO and GA to automatically design Type-1 and Type-2 fuzzy logic controllers , 2015, Int. J. Mach. Learn. Cybern..

[23]  Amin Safari,et al.  A robust PSSs design using PSO in a multi-machine environment , 2010 .

[24]  M. A. Abido,et al.  Robust design of multimachine power system stabilizers using simulated annealing , 2000 .

[25]  Oscar Castillo,et al.  Comparative study of the use of fuzzy logic in improving particle swarm optimization variants for mathematical functions using co-evolution , 2017, Appl. Soft Comput..

[26]  Oscar Castillo,et al.  Comparative Study of Type-2 Fuzzy Particle Swarm, Bee Colony and Bat Algorithms in Optimization of Fuzzy Controllers , 2017, Algorithms.

[27]  A. Dysko,et al.  Enhanced Power System Stability by Coordinated PSS Design , 2010, IEEE Transactions on Power Systems.

[28]  A. M. El-Zonkoly,et al.  Optimal tunning of lead-lag and fuzzy logic power system stabilizers using particle swarm optimization , 2009, Expert Syst. Appl..

[29]  P. Kundur,et al.  Application of Power System Stabilizers for Enhancement of Overall System Stability , 1989, IEEE Power Engineering Review.

[30]  Soheil Ganjefar,et al.  A new PSS tuning technique using ICA and PSO methods with the fourier transform , 2010, 2010 18th Iranian Conference on Electrical Engineering.

[31]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[32]  M. A. Abido Optimal des'ign of Power System Stabilizers Using Particle Swarm Opt'imization , 2002, IEEE Power Engineering Review.