Optimization Based Performance Evaluation of CEIG for Rural Sites

Boora, Shakuntla; Agarwal, S. K.; Sandhu, K. S.

doi:10.1016/j.procs.2018.05.097

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…The performance of 3-phase SEIG or CEIG feeding unbalanced load and single-phase load is accomplished [48]. The performance equations describing the behavior of the capacitor excited induction generator (CEIG) under unbalanced operating conditions/single-phase load conditions is attained analytically using the concept of symmetrical component theory and the sequence equivalent circuits.…”

Section: A Performance Assessment Of 3-phase Iag Furnishing a 1-phasmentioning

confidence: 99%

Performance Assessment Techniques for the 3-Phase IAG

Boora

2019

IJEAT

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This manuscript covers the analytical and optimization based techniques for the performance assessment of 3-phase IAG furnishing 3-phase and 1-phase load. It examines initially the basic phenomenon of voltage build-up and then the steady state performance of 3-phase IAG furnishing 3-phase and 1-phase load. This preliminary study forms the foundation or basis of the design of future controllers. The conventional techniques and MATLAB based optimization technique fsolve is elaborated in detail along-with advantages and disadvantages for attaining the solution of simultaneous non linear equation. The fsolve technique is recommended for the solution of non-linear equations due to its advantages over conventional method.

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Section: A Performance Assessment Of 3-phase Iag Furnishing a 1-phasmentioning

confidence: 99%

Performance Assessment Techniques for the 3-Phase IAG

Boora

2019

IJEAT

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“…Hasanien and Hashem [9] have used cuckoo search algorithm (CSA) to minimize the total impedance equation of wind driven SEIG and assess the steady state performance of the generator. Boora et al [10] have used the concept of symmetrical components with the fsolve algorithm to find the unknown parameters of a capacitive excited induction generator (CEIG) under unbalanced operating conditions. Saha and Sandhu [11] have made a comparison for the implementation of genetic algorithm (GA), particle swarm optimization (PSO) and simulated annealing (SA) algorithm for predicting the steady state performance of SEIG feeding balanced resistive load.…”

Section: Introductionmentioning

confidence: 99%

Optimization algorithms for steady state analysis of self excited induction generator

Athamnah,

Anagreh,

Anagreh

2023

IJECE

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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.

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ANFIS based performance analysis of induction machines

Sharma,

Sharma

2023

Proceedings of the 4th International Computer Sciences and Informatics Conference (Icsic 2022)

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Optimization Based Performance Evaluation of CEIG for Rural Sites

Cited by 3 publications

References 23 publications

Performance Assessment Techniques for the 3-Phase IAG

Performance Assessment Techniques for the 3-Phase IAG

Optimization algorithms for steady state analysis of self excited induction generator

ANFIS based performance analysis of induction machines

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