This manuscript discusses about the Parameter estimation of Induction motor by utilizing the soft computing methodologies that is by using evolutionary algorithms such as Genetic algorithm, Particle swarm optimization, Artificial immune algorithm to overcome the difficulties in the conventional method where we calculating the per phase equivalent circuit parameters from the No load test and Blocked rotor test which compromises in result in terms of accuracy of the result and also evaluated the accuracy of the different algorithm in estimating the parameters of the induction motor.

For the self excited induction generators (SEIG), both the frequency (F) and the magnetizing reactance Xm which depends upon magnetic saturation, vary with load even when the rotor speedis maintained constant. The performance of SEIG can be calculated if the Xm and F are determined accurately. Therefore, a crucial step in the steady-state analysis of the SEIGfor the given machine parameter, speed, excitation capacitance and load impedance is to determine the value of the frequency (F) and the magnetizing reactance (Xm) In the present work, a mathematical model employing the loop impedance method based on graph theory is used which results in simple formulation and is convenient for computer solution. The unknown magnetizing reactance Xmand frequency (F) has been computed using Fuzzy logic based algorithm. The frequency (F) and magnetizing(Xm) are computed separately using real and imaginary parts. The effectiveness of the method is tested through the simulation for simple shunt SEIG and series capacitor compensated SEIGs i.e. short shunt and long shunt SEIGs. The effects of change in capacitance and prime mover speed are also simulated. The analysis presented is validated by experimental results.

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This paper probe about the performance analysis of the Self Excited Induction Generator feeds to linear load for various operating speed condition, capacitance and load condition using Particle swarm optimization (swarm intelligence) which has the advantage of reduced complexity and improved accuracy in solving the equations, the swarm intelligence is investigated for comparing that with the conventional method and Genetic algorithms in order to evaluate the performance of SEIG.