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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

Snapshot of the studied model.
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Fig3: Snapshot of the studied model.

Mentions: The line has been modelled using distributed type parameters, so that more accurate results can be achieved while implementing proposed scheme on very long transmission line. This power system model is being simulated by using the SimPowerSystems toolbox available in Simulink in MATLAB® environment (Demuth et al. 2014). A snapshot of the model being used for studying and obtaining the training and testing 456 data sets is shown in Figure 3. ZP and ZQ are the source impedances of the generators on either side. The three phase V–I measurement block from the SimPowerSystem tool box is used to measure the respective three phase voltages and currents samples at the terminal A. The length of transmission line is 300 km long and the model is simulated for various types of faults at different locations along the transmission line length with different values of the fault resistances. The frequency considered for research work is 50 Hz.Figure 3


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

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

Snapshot of the studied model.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: Snapshot of the studied model.
Mentions: The line has been modelled using distributed type parameters, so that more accurate results can be achieved while implementing proposed scheme on very long transmission line. This power system model is being simulated by using the SimPowerSystems toolbox available in Simulink in MATLAB® environment (Demuth et al. 2014). A snapshot of the model being used for studying and obtaining the training and testing 456 data sets is shown in Figure 3. ZP and ZQ are the source impedances of the generators on either side. The three phase V–I measurement block from the SimPowerSystem tool box is used to measure the respective three phase voltages and currents samples at the terminal A. The length of transmission line is 300 km long and the model is simulated for various types of faults at different locations along the transmission line length with different values of the fault resistances. The frequency considered for research work is 50 Hz.Figure 3

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