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Speed control of SRM supplied by photovoltaic system via ant colony optimization algorithm

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Abstract

This paper proposes a speed control of switched reluctance motor supplied by photovoltaic system. The proposed design of the speed controller is formulated as an optimization problem. Ant colony optimization (ACO) algorithm is employed to search for the optimal proportional integral (PI) parameters of the proposed controller by minimizing the time domain objective function. The behavior of the proposed ACO has been estimated with the behavior of genetic algorithm (GA) in order to prove the superior efficiency of the proposed ACO in tuning PI controller over GA. Also, the behavior of the proposed controller has been estimated with respect to the change of load torque, variable reference speed, ambient temperature and radiation. Simulation results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on GA over a wide range of operating conditions.

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Abbreviations

N r and N s :

Number of rotor and stator poles, respectively

q :

Number of phases

C r :

The commutation ratio

β s and β r :

The stator and rotor pole arc, respectively

I and V :

Module output current and voltage

I c and V c :

Cell output current and voltage

I ph and V ph :

The light generation current and voltage

I s :

Cell reverse saturation current

I sc :

The short circuit current

I o :

The reverse saturation current

R s :

The module series resistance

T :

Cell temperature

K :

Boltzmann’s constant

q o :

Electronic charge

KT :

(0.0017 A/°C) Short circuit current temperature coefficient

G :

Solar illumination in W/m2

E g :

Band gap energy for silicon

A :

Ideality factor

T r :

Reference temperature

I or :

Cell rating saturation current at T r

n s :

Series-connected solar cells

k i :

Cell temperature coefficient

k :

The duty cycle of the pulse width modulation (PWM)

V B and I B :

The output converter voltage and current, respectively

J t :

The objective function

K P and K i :

The parameters of PI controller

n :

Number of nodes

m :

Number of ants

t max :

Maximum iteration

d max :

Maximum distance for each ant’s tour

β :

The relative importance of pheromone versus distance (β > 0)

ρ :

Heuristically defined coefficient (0 < ρ < 1)

α :

Pheromone decay parameter (0 < α < 1)

q a :

Parameter of the algorithm (0 < q a < 1)

τ o :

Initial pheromone level

d i :

Distance between two nodes

u :

Unvisited node

r :

Current node

τ ij :

The pheromone trial deposited between node i and j by ant k

η ij :

The visibility and it equals to the inverse of the distance (η ij  = 1/d ij )

T k :

The path effectuated by the ant k at a given time

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Author information

Authors and Affiliations

  1. Research Institute, Power Electronics and Energy Conversions, NRC Blg., El-Tahrir St., Dokki, Giza, 12311, Egypt

    A. S. Oshaba

  2. Electric Power and Machine Department, Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt

    E. S. Ali & S. M. Abd Elazim

Authors
  1. A. S. Oshaba
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  2. E. S. Ali
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  3. S. M. Abd Elazim
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Corresponding author

Correspondence to S. M. Abd Elazim.

Appendix

Appendix

The parameters of the studied system are as shown below:

  1. (a)

    SRM parameters [26]: N s = 8, N r = 6, rating speed = 13,700 r.p.m, C r = 0.8, q = 4, phase resistance of stator = 17 Ω, phase inductance of aligned position = 0.605 H, phase inductance of unaligned position = 0.1555 H, step angle = 15°.

  2. (b)

    PV parameters [26]: A = 1.2153; E g = 1.11; I or  = 2.35e − 8; I sc = 4.8; T r = 300; K = 1.38e − 23; n s = 36; q o  = 1.6e − 19; k i  = 0.0021.

  3. (c)

    Genetic parameters: max generation = 100; population size = 50; crossover probabilities = 0.75; mutation probabilities = 0.1.

  4. (d)

    ACO parameters: n = 10, m = 5, t max = 5, d max = 49, β = 2, ρ = 0.6, α = 0.1, q a = 0.6, τ o  = 0.1.

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Oshaba, A.S., Ali, E.S. & Abd Elazim, S.M. Speed control of SRM supplied by photovoltaic system via ant colony optimization algorithm. Neural Comput & Applic 28, 365–374 (2017). https://doi.org/10.1007/s00521-015-2068-8

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  • DOI: https://doi.org/10.1007/s00521-015-2068-8

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