Classification of power quality disturbances using Wavelet Transform and Optimized ANN

Muhammad Ijaz; Md Shafiullah; M. A. Abido

doi:10.1109/ISAP.2015.7325522

Classification of power quality disturbances using Wavelet Transform and Optimized ANN

Muhammad Ijaz
, Md Shafiullah
, M. A. Abido

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

32 Scopus citations

Abstract

This paper presents a new approach to detect and classify the power quality disturbance using Wavelet Transform (WT) based Optimized Artificial Neural Network (ANN). The proposed algorithm extracts the energy based feature vector consisting of approximation and detail coefficients of WT. ANN based classifier is used to classify the power quality (PQ) disturbances. Six different types of PQ disturbances are considered to examine the versatility of the proposed approach. Furthermore, a novel and innovative approach is used to optimize the weights of ANN using Differential Evolution (DE). The optimized ANN results demonstrate the superiority, accuracy and robustness of the proposed approach compared to the reported techniques in literature. The comparisons demonstrated that the proposed approach is more superior in terms of classification error reduction and overall accuracy improvement.

Original language	English
Title of host publication	2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509001903
DOIs	https://doi.org/10.1109/ISAP.2015.7325522
State	Published - 10 Nov 2015

Publication series

Name	2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

Differential evolution
Feature extraction
Neural Networks
Power quality
Wavelet Transform

ASJC Scopus subject areas

Artificial Intelligence
Energy Engineering and Power Technology
Computer Science Applications

Access to Document

10.1109/ISAP.2015.7325522

Cite this

Ijaz, M., Shafiullah, M., & Abido, M. A. (2015). Classification of power quality disturbances using Wavelet Transform and Optimized ANN. In 2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015 Article 7325522 (2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISAP.2015.7325522

Ijaz, Muhammad ; Shafiullah, Md ; Abido, M. A. / Classification of power quality disturbances using Wavelet Transform and Optimized ANN. 2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015. Institute of Electrical and Electronics Engineers Inc., 2015. (2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015).

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title = "Classification of power quality disturbances using Wavelet Transform and Optimized ANN",

abstract = "This paper presents a new approach to detect and classify the power quality disturbance using Wavelet Transform (WT) based Optimized Artificial Neural Network (ANN). The proposed algorithm extracts the energy based feature vector consisting of approximation and detail coefficients of WT. ANN based classifier is used to classify the power quality (PQ) disturbances. Six different types of PQ disturbances are considered to examine the versatility of the proposed approach. Furthermore, a novel and innovative approach is used to optimize the weights of ANN using Differential Evolution (DE). The optimized ANN results demonstrate the superiority, accuracy and robustness of the proposed approach compared to the reported techniques in literature. The comparisons demonstrated that the proposed approach is more superior in terms of classification error reduction and overall accuracy improvement.",

keywords = "Differential evolution, Feature extraction, Neural Networks, Power quality, Wavelet Transform",

author = "Muhammad Ijaz and Md Shafiullah and Abido, \{M. A.\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.",

year = "2015",

month = nov,

day = "10",

doi = "10.1109/ISAP.2015.7325522",

language = "English",

series = "2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Ijaz, M, Shafiullah, M & Abido, MA 2015, Classification of power quality disturbances using Wavelet Transform and Optimized ANN. in 2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015., 7325522, 2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ISAP.2015.7325522

Classification of power quality disturbances using Wavelet Transform and Optimized ANN. / Ijaz, Muhammad; Shafiullah, Md ; Abido, M. A.
2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7325522 (2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Classification of power quality disturbances using Wavelet Transform and Optimized ANN

AU - Ijaz, Muhammad

AU - Shafiullah, Md

AU - Abido, M. A.

PY - 2015/11/10

Y1 - 2015/11/10

N2 - This paper presents a new approach to detect and classify the power quality disturbance using Wavelet Transform (WT) based Optimized Artificial Neural Network (ANN). The proposed algorithm extracts the energy based feature vector consisting of approximation and detail coefficients of WT. ANN based classifier is used to classify the power quality (PQ) disturbances. Six different types of PQ disturbances are considered to examine the versatility of the proposed approach. Furthermore, a novel and innovative approach is used to optimize the weights of ANN using Differential Evolution (DE). The optimized ANN results demonstrate the superiority, accuracy and robustness of the proposed approach compared to the reported techniques in literature. The comparisons demonstrated that the proposed approach is more superior in terms of classification error reduction and overall accuracy improvement.

AB - This paper presents a new approach to detect and classify the power quality disturbance using Wavelet Transform (WT) based Optimized Artificial Neural Network (ANN). The proposed algorithm extracts the energy based feature vector consisting of approximation and detail coefficients of WT. ANN based classifier is used to classify the power quality (PQ) disturbances. Six different types of PQ disturbances are considered to examine the versatility of the proposed approach. Furthermore, a novel and innovative approach is used to optimize the weights of ANN using Differential Evolution (DE). The optimized ANN results demonstrate the superiority, accuracy and robustness of the proposed approach compared to the reported techniques in literature. The comparisons demonstrated that the proposed approach is more superior in terms of classification error reduction and overall accuracy improvement.

KW - Differential evolution

KW - Feature extraction

KW - Neural Networks

KW - Power quality

KW - Wavelet Transform

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DO - 10.1109/ISAP.2015.7325522

M3 - Conference contribution

AN - SCOPUS:84962221255

T3 - 2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015

BT - 2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Ijaz M, Shafiullah M , Abido MA. Classification of power quality disturbances using Wavelet Transform and Optimized ANN. In 2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015. Institute of Electrical and Electronics Engineers Inc. 2015. 7325522. (2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015). doi: 10.1109/ISAP.2015.7325522

Classification of power quality disturbances using Wavelet Transform and Optimized ANN

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this