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.
KEYWORDS:
1.
Artificial neural networks
2.
Feedforward networks
3.
Back propagation algorithm
4.
Levenberg–Marquardt algorithm
SOFTWARE: MATLAB/SIMULINK
Figure 1 Snapshot of the studied
model.
Figure 2 Data pre processing illustration.
Figure 3 Regression FIT of the outputs vs.
targets for the network.
In this paper we have studied the
application of artificial neural networks for the detection and classification
of faults on a three phase transmission lines system. The method developed
utilizes the three phase voltages and three phase currents as inputs to the neural
networks. The inputs were normalized with respect to their pre-fault values respectively.
The results shown in the paper is for line to ground fault only. The other types
of faults, e.g. line-to-line, double line-to-ground and symmetrical three phase
faults can be studied and ANNs can be developed for each of these faults. All
the artificial neural networks studied here adopted the back-propagation neural
network architecture. The simulation results obtained prove that the
satisfactory performance has been achieved by all of the proposed neural
networks and are practically implementable.
The importance of choosing the most appropriate ANN configuration, in order to
get the best performance from the network, has been stressed upon in this work.
The sampling frequency adopted for sampling the voltage and current waveforms in
this research work is 1,000 Hz. Some important conclusions that can be drawn
from the research are:
1. Artificial neural networks are a
reliable and effective method for an electrical power system transmission line
fault classification and detection especially in view of the increasing dynamic
connectivity of the modern electrical power transmission systems.
2. The performance of an artificial
neural network should be analyzed properly and particular neural network
structure and learning algorithm before choosing it for a practical
application.
3. Back propagation neural networks
delivers good performance, when they are trained with large training data set,
which is easily available in power systems and hence back propagation networks
have been chosen for proposed method.
The scope of ANN is
wide enough and can be explored more. The fault detection and classification
can be made intelligent by nature by developing suitable intelligent
techniques. This can be achieved if we
have the computers which can handle large amount of data and take least amount
time for calculations.
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