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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

Developed BPNN model in Simulink.
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Fig5: Developed BPNN model in Simulink.

Mentions: Figure 5 depicts the snapshot of the developed ANN model in Simulink of MATLAB. The Neural Network toolbox in Simulink of MATLAB uses the entire data set in three parts. The first part is of date set is known as the training data set, which is used for training purpose of the neural network by computing the gradient and updating the network weights until the network converges for given value of errors. The first part is of date set is known as the validating data set and this validation dataset is used by the network during the training process (this is in the form of inputs only without assigning any outputs values) and the error in validation process for entire validating set is monitored throughout the training process. When the neural network during validation begin the over fitting the given data, the validation errors increase and when the number of validation process fails and increase beyond a particular value, the training process ends to avoid further over fitting the data and the neural network is returned to the minimum number of validation errors. The third part is testing set, the testing data set is generally not used during the training process. The third part is used to judge the overall performance of the finally developed trained neural network. If the test data set reaches up to the minimum value of mean square error at any significantly different iteration than the validation set, it means that the neural network will not be able to provide satisfactory performance and needs to be re-architecture.Figure 5


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

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

Developed BPNN model in Simulink.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig5: Developed BPNN model in Simulink.
Mentions: Figure 5 depicts the snapshot of the developed ANN model in Simulink of MATLAB. The Neural Network toolbox in Simulink of MATLAB uses the entire data set in three parts. The first part is of date set is known as the training data set, which is used for training purpose of the neural network by computing the gradient and updating the network weights until the network converges for given value of errors. The first part is of date set is known as the validating data set and this validation dataset is used by the network during the training process (this is in the form of inputs only without assigning any outputs values) and the error in validation process for entire validating set is monitored throughout the training process. When the neural network during validation begin the over fitting the given data, the validation errors increase and when the number of validation process fails and increase beyond a particular value, the training process ends to avoid further over fitting the data and the neural network is returned to the minimum number of validation errors. The third part is testing set, the testing data set is generally not used during the training process. The third part is used to judge the overall performance of the finally developed trained neural network. If the test data set reaches up to the minimum value of mean square error at any significantly different iteration than the validation set, it means that the neural network will not be able to provide satisfactory performance and needs to be re-architecture.Figure 5

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