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Advances and bibliographic analysis of particle swarm optimization applications in electrical power system: concepts and variants

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Abstract

Power system applications often require solving one or multiple optimization problems which are nonlinear. Due to the limitations such as dimensionality constraints and slow convergence as offered by the analytical methods, swarm intelligence-based methods have emerged as a practical optimization problem solution alternate. Particle swarm optimization (PSO), a well-known member of the swarm intelligence-based family, has been acknowledged as an efficient solution for nonlinear extensive optimization problems. On the one hand, this article provides insights on basic notions and progress of the PSO in power system applications and case studies highlighting the best performance of PSO and its variants. Technical visions essential for the application of PSO, like its form, particle presentation, and the most competent fitness functions, are also depicted for the respective application. On the other hand, it presents a comprehensive bibliographic analysis of 144 high-impact journal articles concerning the prominent applications of PSO as an optimization method in power systems, having more than twenty citations and ordered by the number of citations in descending order. The outcomes of this bibliographic scrutiny demonstrate that Elsevier, IEEE, and Springer articles are the most significant. It also recognizes the most influential and high-impact journals and countries/regions.

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Abbreviations

PSO:

Particle swarm optimization

LP:

Linear programming

NLP:

Nonlinear programming

DP:

Dynamic programming

AI:

Artificial Intelligence

CI:

Computational intelligence

BSOA:

Backtracking search optimization algorithm

RE:

Renewable energy

DG:

Distributed generation

WOA:

Whale optimization algorithm

SKHA:

Stud krill herd algorithm

WT:

Wind turbine

PV:

Photovoltaic

DA:

Dragonfly algorithm

MOEA/D:

Memetic binary differential evolution algorithm

UC:

Unit commitment

CHP:

Combined heat and power

VVC:

volt/var control

ED:

Economic dispatch

SE:

State estimation

OPF:

Optimal power flow

LF:

Load flow

PI:

Proportional integrator

MSE:

Mean-squared error

UC:

Unit commitment

HHO:

Harris Hawk optimizer

TVACPSO:

Time variant acceleration cofficient PSO

BSCA:

Binary Sine Cosine Algorithm

WOA:

Whale optimization algorithm

f :

Objective function

\(J_i (x_i)\) :

Function of particle’s position

A :

Search space

\(g_(x)\) :

Equality constraint function

k :

Number of equality constraint

\(h_(x)\) :

Non-equality constraint functions

m :

Number of non-equality constraints

x :

Particle’s position

v :

Particle’s velocity

t :

Number of iterations

\(\varphi\) :

Acceleration constant

\(p_{i}\) :

Personal bestof the \(i^{th}\) particle

\(p_{g}\) :

Global best of the \(i^{th}\) particle

P :

Probability function

d :

Bit in a string

i :

Index of \(i^{th}\) individual

\(V_{id}\) :

Particle’s velocity vector

\(X_{id}\) :

Particle’s position vector

w :

Weight vector

z :

Measurement vector

T :

Transformer tapping

P :

Active power

Q :

Reactive power

a, b, c :

Positive constants

N :

Number of buses

e :

Error vector

\(\delta\) :

Phase angle

V :

Voltage magnitude

VSI:

Voltage stability index

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Tiwari, S., Kumar, A. Advances and bibliographic analysis of particle swarm optimization applications in electrical power system: concepts and variants. Evol. Intel. 16, 23–47 (2023). https://doi.org/10.1007/s12065-021-00661-3

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