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.
Highlights
The electrical power system consists of so many different complex dynamic and interacting elements, which are always prone to disturbance or an electrical fault
The use of high capacity electrical generating power plants and concept of grid, i.e. synchronized electrical power plants and geographical displaced grids, required fault detection and operation of protection equipment in minimum possible time so that the power system can remain in stable condition
The fault classification method required a neural network that allows it to determine the type of fault from the patterns of pre fault and post fault voltages and currents, which are generated from the values measured from a three phase transmission line of an electrical power system at one terminal
Summary
The electrical power system consists of so many different complex dynamic and interacting elements, which are always prone to disturbance or an electrical fault. A new algorithm based upon ANN is proposed for fast and reliable fault detection and classification. The fault classification method required a neural network that allows it to determine the type of fault from the patterns of pre fault and post fault voltages and currents, which are generated from the values measured from a three phase transmission line of an electrical power system at one terminal. 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.
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