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Multi-verse optimization algorithm- and salp swarm optimization algorithm-based optimization of multilevel inverters

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Abstract

Renewable energy sources are installed into both distribution and transmission grids more and more with the introduction of smart grid concept. Hence, efficient usage of cascaded H-bridge multilevel inverters (MLIs) for power control applications becomes vital for sustainable electricity. Conventionally, selective harmonic elimination equations need to be solved for obtaining optimum switching angles of MLIs. The objective of this study is to obtain switching angles for MLIs to minimize total harmonic distortion. This study contributes to the solution of this problem by utilizing two recently developed intelligent optimization algorithms: multi-verse optimization algorithm and salp swarm algorithm. Moreover, well-known particle swarm optimization is utilized for MLI optimization problem. Seven-level, 11-level and 15-level MLIs are used to minimize total harmonic distortions. Simulation results with different modulation indexes for seven-, 11- and 15-level MLIs are calculated and compared in terms of the accuracy and solution quality. Numerical calculations are verified by using MATLAB/Simulink-based models.

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Abbreviations

\(\omega\) :

The angular velocity

\(\theta _i\) :

The conducting angle of step i

a :

A predefined minimum number

\(a_i, \ldots , y_i\) :

The objects in the universes

b :

A predefined maximum number

\(c_1\) :

Exploration and exploitation parameter in SSA

\(c_2\) :

Random number in the interval [0,1]

\(c_3\) :

Random number in the interval [0,1]

d :

The number of objects

\(F_j\) :

The position of the food source

k :

The number of separate DC sources

L :

Maximum iteration number

l :

Current iteration number

\(l_i\) :

Learning factors

\({\text {lb}}_j\) :

The lower bound for the variables

\({\text {loc}}\) :

Location of the particle

\(m_1\) :

Modulation index

\(N_s\) :

Number of salps

\({\text {NI}}(U_i)\) :

The normalized inflation rate of universe i

p :

Exploitation factor

\(r_i\) :

Random number between 0 and 1

s :

Number of unknowns in SSA

Spd:

Velocity of the particle

T :

Maximum number of iterations

t :

The current iteration

\(u_i\) :

The ith universe

\({\text {ub}}_j\) :

The upper bound for the variables

\(v_0\) :

The initial speed

\(V_i\) :

The voltage component i

\(V_{\mathrm{dc}}\) :

The voltage magnitude of the DC source

\(V_{\mathrm{Lmax}}\) :

The maximum attainable amplitude of the inverter

\(V_{L}^*\) :

The amplitude command of the inverter for a sine wave output phase voltage

\(V_{\mathrm{par}}\) :

A parameter showing the ratio of final speed to initial speed

w :

Inertia weight

\(x_j^1\) :

The leader salp

\(x_{i}^{j}\) :

The jth object in the universe i

\(x_{k}^{j}\) :

The jth object in the universe k selected by roulette wheel mechanism

\(z_i\) :

The object values obtained by transfer operation using wormholes

ANN:

Artificial neural network

CPB:

Cement paste backfill

DC:

Direct current

DE:

Differential evolution

FFT:

Fast Fourier transform

GWO:

Grey wolf optimization

HB:

H-bridge

MLI:

Multilevel inverter

MVO:

Multi-verse optimization

PSO:

Particle swarm optimization

SSA:

Salp swarm algorithm

TDR:

Travelling distance rate

THD:

Total harmonic distortion

WEP:

Wormhole existence probability

WOA:

Whale optimization algorithm

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  1. Management Information Systems Department, Kadir Has University, İstanbul, Turkey

    Oğuzhan Ceylan

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Ceylan, O. Multi-verse optimization algorithm- and salp swarm optimization algorithm-based optimization of multilevel inverters. Neural Comput & Applic 33, 1935–1950 (2021). https://doi.org/10.1007/s00521-020-05062-8

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