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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

Structure of back propagation of ANN.
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Fig1: Structure of back propagation of ANN.

Mentions: In the Back propagation neural network (BPNN) the output is feedback to the input to calculate the change in the values of weights. One of the major reasons for taking the back propagation algorithm is to eliminate the one of the constraints on two layers ANNs, i.e. similar inputs lead to the similar output. The error for each iteration and for each point is calculated by initiating from the last step and by sending calculated the error backwards. The weights of the back-error-propagation algorithm for the neural network are chosen randomly, feeds back in an input pair and then obtain the result. After each step, the weights are updated with the new ones and the process is repeated for entire set of inputs-outputs combinations available in the training data set provided by developer. This process is repeated until the network converges for the given values of the targets for a pre defined value of error tolerance. The entire process of back propagation can be understood by Figure 1. The back-error-propagation algorithm is effectively used for several purposes including its application to error functions (other than the sum of squared errors) and for the calculation of Jacobian and Hessian matrices. This entire process is adopted by each and every layer in the entire the network in the backward direction (Haykin 1994). The proposed algorithm uses the Mean Square Error (MSE) technique for calculating the error in each iteration.


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

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

Structure of back propagation of ANN.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: Structure of back propagation of ANN.
Mentions: In the Back propagation neural network (BPNN) the output is feedback to the input to calculate the change in the values of weights. One of the major reasons for taking the back propagation algorithm is to eliminate the one of the constraints on two layers ANNs, i.e. similar inputs lead to the similar output. The error for each iteration and for each point is calculated by initiating from the last step and by sending calculated the error backwards. The weights of the back-error-propagation algorithm for the neural network are chosen randomly, feeds back in an input pair and then obtain the result. After each step, the weights are updated with the new ones and the process is repeated for entire set of inputs-outputs combinations available in the training data set provided by developer. This process is repeated until the network converges for the given values of the targets for a pre defined value of error tolerance. The entire process of back propagation can be understood by Figure 1. The back-error-propagation algorithm is effectively used for several purposes including its application to error functions (other than the sum of squared errors) and for the calculation of Jacobian and Hessian matrices. This entire process is adopted by each and every layer in the entire the network in the backward direction (Haykin 1994). The proposed algorithm uses the Mean Square Error (MSE) technique for calculating the error in each iteration.

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