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Identification of Nonlinear Synchronous Generator Parameters Using Stochastic Fractal Search Algorithm

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Abstract

This paper develops a general output error identification approach of nonlinear synchronous generator parameters. The considered method is based on a meta-heuristic optimization algorithm called stochastic fractal search (SFS). It consists of minimizing a quadratic criterion that represents the squared difference between the simulated model outputs and those obtained from the model to be identified by using the SFS algorithm. To highlight the performance of the proposed method, a deep comparison with the PSO algorithm was carried out. The obtained results proved the superiority of the proposed approach in terms of stability, robustness, estimation accuracy and convergence speed.

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Authors and Affiliations

  1. Automatic Laboratory (LAS), Electrical Engineering Department, Setif 1 University, Setif, Algeria

    Elrachid Bendaoud & Hammoud Radjeai

  2. Intelligent Systems Laboratory, Electronics Department, Setif 1 University, Setif, Algeria

    Oussama Boutalbi

Authors
  1. Elrachid Bendaoud
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  2. Hammoud Radjeai
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  3. Oussama Boutalbi
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Correspondence to Elrachid Bendaoud.

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Appendices

Appendices

1.1 A. SFS and PSO optimization parameters

Parameters

SFS

PSO

Population size PZ

300

300

Iteration number \(N_\mathrm{iter}\)

50

50

Maximum diffusion number MDN

2

−

Gaussian walks selection probability \(P_{walks}\)

0

−

Particle learning coefficient \(C_{1}\)

−

2

Population learning coefficient \(C_{1}\)

−

2.01

Inertia weight \(w_{\min }\)

−

0.4

Inertia weight \(w_{\max }\)

−

0.9

1.2 B. Notation

OE-SFS:

Output error method based on stochastic fractal search algorithm

SFS:

Stochastic fractal search

PSO:

Particle swarm optimization

SSFR:

Stand still frequency repose

PZ :

Population size

MDN :

Maximum diffusion number

PRBS :

Pseudo-random binary sequence

P.U:

Per unit

Std :

Standard deviation

MFV:

Mean fitness value

SDFV:

Standard deviation of fitness value

AWGN

Additive white Gaussian noise

\(N_\mathrm{iter}\):

Iteration number

\(\theta \), \(\hat{\theta }\)

Actual and identified parameters vector

\(T_\mathrm{s}\)

Sampling times

\(T_\mathrm{f}\)

Sampling procedure

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Bendaoud, E., Radjeai, H. & Boutalbi, O. Identification of Nonlinear Synchronous Generator Parameters Using Stochastic Fractal Search Algorithm. J Control Autom Electr Syst 32, 1639–1651 (2021). https://doi.org/10.1007/s40313-021-00804-y

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