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Fault detection and classification in electrical power transmission system using artificial neural network.

Jamil M, Sharma SK, Singh R - Springerplus (2015)

Bottom Line: The three phase currents and voltages of one end are taken as inputs in the proposed scheme.A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network.The various simulations and analysis of signals is done in the MATLAB(®) environment.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Faculty of Engineering, Jamia Millia Islamia, New Delhi, 110025 India.

ABSTRACT
This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

No MeSH data available.


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Basic structure of back-error-propagation algorithm.
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Fig2: Basic structure of back-error-propagation algorithm.

Mentions: The MSE for each output in each iteration is calculated by\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MSE = \frac{1}{N}\sum\limits_{1}^{N} {\left( {E_{i} - E_{o} } \right)}^{2}$$\end{document}MSE=1N∑1NEi-Eo2where N is number of iterations, Ei is actual output and Eo is out of the model. This entire architecture of back propagation based ANN is illustrated in Figure 2, which shows the each and every step of algorithm.Figure 2


Fault detection and classification in electrical power transmission system using artificial neural network.

Jamil M, Sharma SK, Singh R - Springerplus (2015)

Basic structure of back-error-propagation algorithm.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496419&req=5

Fig2: Basic structure of back-error-propagation algorithm.
Mentions: The MSE for each output in each iteration is calculated by\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MSE = \frac{1}{N}\sum\limits_{1}^{N} {\left( {E_{i} - E_{o} } \right)}^{2}$$\end{document}MSE=1N∑1NEi-Eo2where N is number of iterations, Ei is actual output and Eo is out of the model. This entire architecture of back propagation based ANN is illustrated in Figure 2, which shows the each and every step of algorithm.Figure 2

Bottom Line: The three phase currents and voltages of one end are taken as inputs in the proposed scheme.A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network.The various simulations and analysis of signals is done in the MATLAB(®) environment.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Faculty of Engineering, Jamia Millia Islamia, New Delhi, 110025 India.

ABSTRACT
This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

No MeSH data available.


Related in: MedlinePlus