Optimal design of power system stabilizers using a small population based PSO

2007 IEEE Symposium on Artificial Life

2007

Power System Stabilizers (PSSs) are used to damp intra-area and inter-area oscillations in a power network. They provide effective supplementary control by supplying auxiliary control signals to the excitation system of the generators. The proper tuning of PSSs has a significant influence on its effectiveness in providing the required damping under different operating conditions and disturbances. Various algorithms have been successfully implemented to simultaneously design multiple optimal PSSs in power systems. As the power network's operating conditions change, the performance of PSSs degrade.

Section: Two-area Multi-machine Power Systemmentioning

confidence: 99%

Adaptive Power System Stabilizers Using Artificial Immune System

Hunjan

2007 IEEE Symposium on Artificial Life

2007

“…It can be observed from these figures that the dual input controller (Controller 2) provides better damping than the single input controller (Controller 1). The given tables also compare the performance of the controller shown in Figs. [5] - [10] based on their area under the speed responses. …”

Section: Testmentioning

confidence: 99%

“…A Small Population based Particle Swarm Optimization (SPPSO) algorithm has been used in this paper as a technique to optimize the parameters of SVC damping controllers in a two area power system. The effectiveness of the SPPSO algorithm to optimize parameters of PSSs has been reported in [10]. It is a population based algorithm which does not require individuals/particles to reproduce at every generation but keeps evolving better solutions.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of SVC Damping Controllers with Wide Area Measurements Using Small Population based PSO

Das

Jetti

The 2006 IEEE International Joint Conference on Neural Network Proceedings

2006

Self Cite

“…These include Differential Evolution (DE) [5]- [6], Artificial Immune Systems (AIS) [7], Bacterial Foraging Algorithm (BFA) [8], Ant Colony (ACO) [9], and Particle Swarm Optimization (PSO) [10] which belong to the family of Swarm intelligence. Among all these algorithms, PSO has been widely used for parameter optimization in controller design [11]- [12]. PSO is simple and easy to implement; however, the algorithm is very sensitive to some of its parameters such as inertia weights, and acceleration factors.…”

Section: Introductionmentioning

confidence: 99%

Power system controller design using multi-population PBIL

Folly

2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG)

2013

The application of a multi-population based Population-Based Incremental Learning (PBIL) to power system controller design is presented in this paper. PBIL is a combination of evolutionary optimization and competitive learning derived from artificial neural networks. Single population PBIL has recently received increasing attention in various engineering fields due to its effectiveness, easy implementation and robustness. Despite these strengths, PBIL still suffers from issues of loss of diversity in the population. The use of multi-population is seen as one way of increasing the diversity in the population. The approach is applied to power system controller design. Simulations results show that the multipopulation PBIL approach performs better than the standard PBIL and is as effective as PBIL where adaptive learning is used.