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Frequency Control of 5 kW Self-excited Induction Generator Using Gravitational Search Algorithm and Genetic Algorithm

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AI and IOT in Renewable Energy

Part of the book series: Studies in Infrastructure and Control ((sic))

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Abstract

For harnessing the wind energy, self-excited induction generator is becoming more popular in today’s scenario. Nonlinear loads lead to major drawbacks in self-excited induction generator such as poor voltage, frequency regulation and reactive power consumption. This poor voltage and frequency of SEIG depends on many factors like types of load, capacitance involved for reactive power compensation and prime mover speed. The improved performance of SEIG can be obtained by using steady-state analysis of equivalent circuit and usage of optimization techniques in SEIG machine. The main objective of this chapter to select the values of shunt and series capacitances at specified speed in order to achieve an optimum frequency regulation using gravitational search algorithm (GSA) and genetic algorithm (GA). GSA works on Newton’s law of gravity, whereas GA follows the steps of selection, crossover and mutation. Both the techniques are based on heuristic approach of gbest and pbest. Therefore, this study is carried out on an objective function of relative mean error of frequency regulation. Correspondingly, the minimum fitness is calculated for resistive and resistive-inductive load. The improved performance validates both the optimization techniques.

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Abbreviations

SEIG:

Self-excited Induction Generator

STATCOM:

Static synchronous compensator

ANFIS:

Adaptive neuro-fuzzy inference system

VSC:

Voltage source converter

SUMT:

Sequential unconstrained minimization technique

GSA:

Gravitational search algorithm

GA:

Genetic algorithm

Xro:

Per unit blocked rotor reactance

R1, R2:

Per phase stator and rotor resistance in per unit

X1, X2:

Per phase stator and rotor leakage reactance in per unit

Xm, Xmun:

Per unit magnetizing and unsaturated magnetizing reactance

F, v:

Per unit frequency and prime moverspeed

Csh, Cs:

Shunt and series capacitance in microfarad

Xmmn:

Minimum value of magnetizing reactance in per unit

Xmmx:

Maximum value of magnetizing reactance in per unit

Xc:

Shunt capacitance in per unit

Fmn:

Minimum value of frequency in per unit

Fmx:

Maximum value of frequency in per unit

Is:

Slip factor

Vs:

Stator voltage in per unit

Vgen:

Air gap voltage in per unit

Is:

Stator current in per unit

Il:

Load current in per unit

References

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Amity University, Noida, UP, India

    Swati Paliwal & Sanjay Kumar Sinha

  2. Department of Electrical Engineering, Kamla Nehru Institute of Technology, Sultanpur, India

    Yogesh Kumar Chauhan

Authors
  1. Swati Paliwal
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  2. Sanjay Kumar Sinha
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  3. Yogesh Kumar Chauhan
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Corresponding author

Correspondence to Swati Paliwal .

Editor information

Editors and Affiliations

  1. School of Electrical, Electronic and Communication Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

    Rabindra Nath Shaw

  2. Senior Electrical Engineer-wind, solar and energy storage, DNV GL Docklands, Melbourne, VIC, Australia

    Nishad Mendis

  3. Department of Electrical Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Saad Mekhilef

  4. School of Engineering and Applied Sciences, The Neotia University, Kolkata, West Bengal, India

    Ankush Ghosh

Appendices

Appendix 1: Specification of Self-excited Induction Generator

5 kW, 415 V, 10.1 A line, Δ connected, 4 pole, 50 Hz Rs = 0.0532 pu, Rr = 0.0337 pu, Xs = 0.0533 pu, Xr = 0.0733 pu, Xmuns = 3.48 pu.

Appendix 2: Coefficients of Ztotal

The coefficients P and Q are defined as

$$\begin{aligned} P_{1} & = - X_{1} *R_{L} *(X_{2} + Xm) - X_{2} *R_{L} *Xm \\ P_{2} & = X_{1} *R_{L} *v*(X_{2} + Xm) + X_{2} *R_{L} *Xm*v \\ P_{3} & = R_{1} *R_{2} *R_{L} + X_{1} *X_{c} *R_{2} + (R_{1} *X_{c} + R_{L} *X_{c} )*(X_{2} + Xm) \\ & \quad + R_{2} *X_{m} *X_{c} \\ P_{4} & = (( - v*R_{1} *X_{c} ) - v*R_{L} *X_{c} )*(X_{2} + Xm) \\ \end{aligned}$$
$$\begin{aligned} Q_{1} & = 0 \\ Q_{2} & = X_{1} *R_{L} *R_{2} + (X_{2} + Xm)*(R_{1} *X_{c} + R_{L} *X_{c} ) + Xm*R_{2} *R_{L} \\ & \quad + Xm*X_{c} *X_{2} \\ Q_{3} & = (X_{2} + Xm)*( - R_{1} *R_{L} *v - X_{1} *X_{c} *v)*Xm*X_{c} *X_{2} *v \\ Q_{4} & = - R_{L} *Xc*R_{2} - R_{1} *R_{2} *Xc \\ \end{aligned}$$

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Paliwal, S., Sinha, S.K., Chauhan, Y.K. (2021). Frequency Control of 5 kW Self-excited Induction Generator Using Gravitational Search Algorithm and Genetic Algorithm. In: Shaw, R.N., Mendis, N., Mekhilef, S., Ghosh, A. (eds) AI and IOT in Renewable Energy. Studies in Infrastructure and Control. Springer, Singapore. https://doi.org/10.1007/978-981-16-1011-0_8

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  • DOI: https://doi.org/10.1007/978-981-16-1011-0_8

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  • Online ISBN: 978-981-16-1011-0

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