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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

Confusion matrices for training, testing and validation phases.
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Fig8: Confusion matrices for training, testing and validation phases.

Mentions: Another means of testing the performance of the neural network is to plot the confusion matrices for the various types of errors that occurred for the trained neural network. Figure 8 plots the confusion matrix for the three phases of training, testing and validation. The diagonal cells in green indicate the number of cases that have been classified correctly by the neural network and the off-diagonal cells which are in red indicate the number of cases that have been wrongly classified by the ANN. The last cell in blue in each of the matrices indicates the total percentage of cases that have been classified correctly in green and the vice versa in red. It can be seen that the chosen neural network has 100 percent accuracy in fault detection.Figure 8


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

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

Confusion matrices for training, testing and validation phases.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig8: Confusion matrices for training, testing and validation phases.
Mentions: Another means of testing the performance of the neural network is to plot the confusion matrices for the various types of errors that occurred for the trained neural network. Figure 8 plots the confusion matrix for the three phases of training, testing and validation. The diagonal cells in green indicate the number of cases that have been classified correctly by the neural network and the off-diagonal cells which are in red indicate the number of cases that have been wrongly classified by the ANN. The last cell in blue in each of the matrices indicates the total percentage of cases that have been classified correctly in green and the vice versa in red. It can be seen that the chosen neural network has 100 percent accuracy in fault detection.Figure 8

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