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Neural Computing and Applications

, Volume 31, Supplement 1, pp 543–556 | Cite as

Comprehensive identification of multiple harmonic sources using fuzzy logic and adjusted probabilistic neural network

  • Amir Moradifar
  • Asghar Akbari ForoudEmail author
  • Khalil Gorgani Firouzjah
Original Article
  • 127 Downloads

Abstract

This paper presents a comprehensive approach based on fuzzy logic and probabilistic neural network (PNN) to identify location, relative level, and type of multiple harmonic sources in power distribution systems. The location and relative level of harmonic sources were determined in the fuzzy stage by interpreting harmonic powers together with network impedances. Then, the type of the harmonic sources was classified in the neural stage using adjusted PNN. In the proposed method, the harmonic powers were considered as classification features. Then, ReliefF feature selection method was used to reduce the redundant data and dimension of features vector. A new modified adaptive imperialist competitive algorithm (MAICA) was proposed to determine the only adjusted parameter of the PNN classifier. Furthermore, a deep belief network (DBN) was applied in the neural stage, and its results were compared with the PNN classifier. The proposed approach was evaluated on IEEE 18-bus and IEEE 69-bus test systems. Unlike the single point methods, the presented method provides information on multiple harmonic sources in the whole of the distribution system. The results show that the comprehensive approach identifies the multiple harmonic sources with high accuracy.

Keywords

Fuzzy logic Probabilistic neural network Multiple harmonic sources Comprehensive approach Imperialist competitive algorithm 

Nomenclature

h

Index of harmonic order

b , n

Indices of all busses

r , i

Indices of real and imaginary parts of mathematical symbols

H

Highest order harmonic

B

Number of busses

\( {\left({I}_b^h\right)}_{NLL} \)

Harmonic current of NLL at bus b

\( {\left({I}_b^{h, r}\right)}_{NLL} \)

Real harmonic current of NLL at bus b

\( {\left({I}_b^{h, i}\right)}_{NLL} \)

Imaginary harmonic current of NLL at bus b

\( {\left({I}_b^h\right)}_{b us} \)

Injected harmonic into the system from bus b

\( {V}_b^h \)

Harmonic voltage at bus b

\( {y}_{b, n}^h \)

Admittance of line connecting the busses b and n

\( {Z}_{b, b}^h \)

Diagonal element of network impedance matrix

\( {Z}_{b, b}^{h, r} \)

Real part of harmonic impedance at bus b

\( {Z}_{b, b}^{h, i} \)

Imaginary part of harmonic impedance at bus b

\( {\mathrm{z}}_{\mathrm{b},0}^{\mathrm{h},\mathrm{i}} \)

Imaginary part of impedance between bus b and ground

L, C

Inductance and capacitance

\( {S}_b^h \)

Harmonic power at bus b

\( {P}_b^h \)

Real harmonic power at bus b

\( {Q}_b^h \)

Imaginary harmonic power at bus b

HLI

Harmonic localization index

\( \overline{\overline{HLI}} \)

Maximum harmonic localization index

col

Colony position

imp

Imperialist position

c

Index of all colonies

e

Index of all imperialists

β

Assimilation coefficients

β1 , β2

Internal and external assimilation coefficients

β2b , β2w

External assimilation coefficients of the best and worst imperialist

rand

Random number between “0” and “1”

iter

Iteration

itermax

Maximum iteration

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Faculty of Electrical & Computer EngineeringSemnan UniversitySemnanIran
  2. 2.Faculty of Electrical & Computer EngineeringUniversity of MazandaranBabolsarIran

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