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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 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.

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

Curve of regression Fit for the outputs vs. targets of the proposed ANN.
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Fig10: Curve of regression Fit for the outputs vs. targets of the proposed ANN.

Mentions: The performance of the trained neural network is tested in two ways, i.e. first by plotting the linear regression that relates the targets to the outputs as shown in Figure 10. The correlation coefficient in this case was found to be 0.93788 which indicates satisfactory correlation between the targets and the outputs.Figure 10


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

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

Curve of regression Fit for the outputs vs. targets of the proposed ANN.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig10: Curve of regression Fit for the outputs vs. targets of the proposed ANN.
Mentions: The performance of the trained neural network is tested in two ways, i.e. first by plotting the linear regression that relates the targets to the outputs as shown in Figure 10. The correlation coefficient in this case was found to be 0.93788 which indicates satisfactory correlation between the targets and the outputs.Figure 10

Bottom Line: 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.

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