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Steady-state analysis of self-excited induction generators

Steady-state analysis of self-excited induction generators

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The aim of this chapter is to show how a metaheuristic optimization technique such as GA can be applied in solving a nonoptimization problem, i.e., in determining the steady-state performances of a SEIG for general operating conditions. GA-based optimization procedure is applied to the analysis of SEIG feeding balanced/unbalanced and static/dynamic loads. The GA has been applied to the computation of the unknowns by minimizing the total impedance module of the equivalent passive circuit.

Chapter Contents:

  • 14.1 Introduction
  • 14.2 System configuration
  • 14.3 Induction generator model
  • 14.4 Steady-state equations of SEIG
  • 14.5 Steady-state equations of parallel operated SEIGs
  • 14.6 Solution method
  • 14.6.1 Overview of genetic algorithm
  • 14.6.2 Application of GA to SEIG
  • 14.6.3 Application of GA to parallel operated SEIGs
  • 14.6.3.1 Objective function
  • 14.6.3.2 Control variables
  • 14.6.3.3 Performance equations
  • 14.7 Simulation results
  • 14.7.1 Steady-state analysis of SEIG
  • 14.7.1.1 Balanced/unbalanced operation
  • 14.7.1.2 Voltage control of the SEIG feeding a static load
  • 14.7.1.3 Voltage control of the SEIG feeding a dynamic load
  • 14.7.2 Steady-state analysis of parallel operated SEIGs
  • 14.7.2.1 Basic approach
  • 14.7.2.2 Controlling terminal voltage by excitation capacitance
  • 14.7.2.3 Controlling terminal voltage by SEIGs speeds
  • 14.8 Conclusion
  • References

Inspec keywords: asynchronous generators; genetic algorithms

Other keywords: metaheuristic optimization technique; GA-based optimization procedure; steady-state analysis; SEIG; steady-state performances; nonoptimization problem; self-excited induction generators; genetic algorithm

Subjects: Optimisation techniques; Asynchronous machines

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