Genetic Algorithm Based Performance Analysis of Self Excited Induction Generator (original) (raw)
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International Journal of Sustainable Energy, 2011
The main focus of this paper is the steady-state analysis of self-excited induction generators (SEIGs). It employs the genetic algorithm approach (GAA) to estimate the steady-state performance of these machines. Further, the GAA is used for the solution of problems related to the operation of a number of SEIGs running in parallel. GA-based modelling is found to be effective to determine the generated voltage and frequency. Experimental results validate the proposed methodology.
Voltage Control of Self-Excited Induction Generator using Genetic Algorithm
2009
Self-excited induction generators (SEIG) are found to be most suitable candidate for wind energy conversion application required at remote windy locations. Such generators are not able to maintain the terminal voltage with load as, a literature survey reveals, the voltage profile falls sharply with load. In this paper an attempt has been made to improve the voltage profile of a self-excited induction generator. A new methodology based upon Genetic Algorithm (GA) is proposed to compute the steady state performance of the model including core loss branch. Further efforts are made to control the terminal voltage under loaded conditions. Simulated results using proposed modeling have been compared with experimental results. A close agreement between the computed and experimental results confirms the validity of the approach adopted.
Genetic Algorithm Based Control System Design of a Self-Excited Induction Generator
2006
Studies of self-excited induction generators have been investigated since 1935. Many papers dealing with various problems in the field of SEIG have been published. The primary advantages of SEIG are lower maintenance costs, better transient performance, lack of a dc power supply for field excitation, brushless construction (squirrel-cage rotor), etc. In addition, induction generators have been widely employed to operate as wind-turbine generators and small hydroelectric generators of isolated power systems. Induction generators can also be connected to large power systems, to inject electric power .
Wind Engineering, 2017
In order to provide the reactive power demand of a self-excited induction generator which is required to achieve a voltage build-up, a three-phase capacitor bank is connected between the generator terminals. As loading increases, the operating point on the magnetizing curve moves toward the linear region which may lead to the collapse of the generated voltage. In this article, genetic algorithms are used to evaluate the value of the excitation capacitance that makes the machine operate in the saturation region which ensures a stable generated voltage. To verify the effectiveness of this method, a laboratory machine which has a relatively high stator and rotor resistances and leakage reactance is considered. The values of the excitation capacitances predicted by the genetic algorithms are applied to the machine Simulink-based model. The results obtained by the simulation are compared with experimental results which show a good agreement.
A new algorithm applied to the evaluation of self excited induction generator performance
Proceedings of 12th WSEAS …, 2010
The paper presents the application of DIRECT algorithm to analyse the performances of the Self-excited induction generator. It is used to minimize the induction generator admittance yielding the solution which consists of the magnetizing reactance and the frequency. These parameters are the keys to find out the self excitation process requirements in terms of the prime mover speed, the capacitance and the load impedance and finally the output performances such as the voltage, output power, etc. A comparison with other powerful optimization algorithms is investigated to obtain DIRECT algorithm performances.
Optimization algorithms for steady state analysis of self excited induction generator
International Journal of Electrical and Computer Engineering (IJECE), 2023
The current publication is directed to evaluate the steady state performance of three-phase self-excited induction generator (SEIG) utilizing particle swarm optimization (PSO), grey wolf optimization (GWO), wale optimization algorithm (WOA), genetic algorithm (GA), and three MATLAB optimization functions (fminimax, fmincon, fminunc). The behavior of the output voltage and frequency under a vast range of variation in the load, rotational speed and excitation capacitance is examined for each optimizer. A comparison made shows that the most accurate results are obtained with GA followed by GWO. Consequently, GA optimizer can be categorized as the best choice to analyze the generator under various conditions.
Electric Power Systems Research, 2012
The paper presents the application of DIRECT algorithm to analyse the performance of the Self-excited induction generator (SEIG). To the author best knowledge, this is the first attempt to apply it to such a problem. DIRECT algorithm is used to minimize the induction generator's admittance without the need to separate it into its real and imaginary parts. No initial guess is required as it needs only the upper and lower values of the unknown variables which are easily determined. The obtained minimum admittance yields the adequate magnetizing reactance and the frequency. These two key parameters are then used to compute the self-excitation process requirements in terms of the prime mover speed, the capacitance and the load impedance on the one hand and to predict the generator steady state performance parameters on the other. Very good agreement between predicted results and experimental measurements is achieved.
Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator
Journal of Computer Science and Technology, 2006
It is observed that conventional techniques to analyse the steady state analysis of Self-Excited Induction Generator (SEIG) involve cumbersome mathematical procedures. In this paper an Artificial Intelligence (AI) technique has been used to analyse the behaviour of Self-Excited Induction Generator, which does not require rigorous modelling as required in conventional techniques. Proposed Artificial Neural Network (ANN) model has been implemented to predict the effect of speed, capacitance and load on generated voltage and frequency of SEIG. Experimental data is used for the training of ANN. Results obtained from the trained ANN are found to be in close agreement with the experimental results.