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Advanced Adaptive Particle Swarm Optimization Based Svc Controller For Power System Stability

1240 words - 5 pages

SVC’s are first generation FACTS controllers which are expected to revolutionize power transmission in future. SVC provides an excellent source of rapidly controllable reactive shunt compensation for dynamic voltage control through its utilization of high-speed thyristor switching controlled devices. SVC is typically made up of coupling transformer, thyristor valves, reactors, capacitance (often tuned for harmonic filtering)[4].They are used to control the voltage profile under load variations, increase power transfer capability and improve system stability[28]. They can be used for damping power system oscillations incorporating some auxiliary ...view middle of the document...

It is a heuristic & stochastic based optimization technique. PSO can be used on optimization problem that are partially irregular, noisy, change over time, etc. It is developed from swarm intelligence and is based on the research of bird and fish flock movement behavior .The particle swarm optimization consists of swarm of particles which are initialized with a population of random candidate solution in the multidimensional search space. During their flying movement they follow the trajectory according to their own best flying experience (pbest) & best flying experience of the group (gbest). During this process, each particle modify its position (eq. 1)& velocity (eq. 2) according to shared information to follow the best trajectory leads to optimum solution & this technique is simple & very few parameters need to be determined. The choice of PSO parameters can have a large impact on optimization performance. Selecting PSO parameters that yield good performance has therefore been the subject of much research. The PSO algorithm consists of just three steps, which are repeated until some stopping condition is met.
1. Initialize the number of particles, initial particle position & velocity.

, while and are maximum and minimum limits for the d-dimension search space.
Thereafter evaluate the fitness of each particle and define pbest & gbest.
2. Update velocity and position of each particle.
c1 and c2 represents the acceleration factors, and represents distributed random numbers between (0,1).First part of equation (1) depicts the previous velocity of the particle, the second part is a positive cognitive component & third part is a positive social component as described in [26].
3. Update individual (pbest) and global best (gbest) fitness and position.
vidk and xidk defines separately the velocity & position of the particle i at its k times iteration and the d-dimension quality of its position:
pbestidk represents the d-dimension quality of the individual i at its best optimized position at its k times.
gbestdk represents the d-dimension quality of the swarm at its best optimized position.


[1] N. G. Hingorani, L. Gyugyi, “Understanding FACTS”, IEEE Press, 2001.
[2] P. Kundur, “Power System Stability and Control”, Tata McGraw-Hill, 2006.
[3] K. R. Padiyar, Power System Dynamics Stability and Control, BS Publications, 2nd Edition, Hyderabad, India, 2002
[4] Y. H. Song, A. T. Johns, “Flexible AC Transmission Systems (FACTS)”, IET, 2009
[5] Poonam Singhal, S.K.Aggarwal and Narender Kumar, ‘Transient Stability Enhancement using UPFC.’IREMOS,2013.
[6] Y. Mishra,S. Mishra and Fangxing Li ‘Coordinated Tuning of DFIG-Based Wind Turbines and Batteries Using Bacteria Foraging Technique for Maintaining Constant Grid Power Output’.
[7] S. Panda, N. P. Padhy, “Comparison of Particle Swarm Optimization and Genetic Algorithm for TCSC-based Controller Design” International...

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