The document discusses utilizing a wavelet-assisted neural network for analyzing faults in transmission lines, focusing on abnormal voltage and current values to determine appropriate protective measures. It describes the use of discrete wavelet transform (DWT) for signal analysis and the implementation of a back-propagation neural network (BPN) for fault detection and prediction based on training patterns. The study highlights the performance of the neural network in identifying different fault conditions and provides various fault parameter values.

1. Wavelet Assisted Neural Network for
Wavelet Assisted Neural Network for
Transmission Line Fault Analysis
Transmission Line Fault Analysis

2. Purpose of Fault Analysis:
Purpose of Fault Analysis:
 To determine the abnormal values of voltages and currents.
To determine the abnormal values of voltages and currents.
 To select appropriate protective scheme, relay and circuit
To select appropriate protective scheme, relay and circuit
breaker in order to save the system from the abnormal
breaker in order to save the system from the abnormal
condition within minimum time.
condition within minimum time.

3. 3
3
Effects of Faults:
Effects of Faults:
 Faults gives rise to abnormal operating condition.
Faults gives rise to abnormal operating condition.
 Insulation breakdown beyond their rated value due to over
Insulation breakdown beyond their rated value due to over
voltage.
voltage.
 Large current result in overheating of power system component.
Large current result in overheating of power system component.
 Damage or disrupt power supply.
Damage or disrupt power supply.

4. 4
4
Fig. 2 : Power Transmission Network Under Study
Fig. 2 : Power Transmission Network Under Study
 KTPS - Kolaghat Thermal Power Station
KTPS - Kolaghat Thermal Power Station
 HPCL - Haldia Petrochemical Limited
HPCL - Haldia Petrochemical Limited
 FCI - Food Corporation India
FCI - Food Corporation India
Prototype Power System and Fault Simulation
Prototype Power System and Fault Simulation

5. 5
5
Discrete Wavelet Transform
Discrete Wavelet Transform
 In DWT, a time scale representation of a signal is obtained using
In DWT, a time scale representation of a signal is obtained using
digital filtering techniques.
digital filtering techniques.
 Different Cutoff Frequencies are used to analyze the signal at different
Different Cutoff Frequencies are used to analyze the signal at different
scales.
scales.
 The approximation is the high-scale, low-frequency component of the
The approximation is the high-scale, low-frequency component of the
signal. The detail is the low-scale, high-frequency components.
signal. The detail is the low-scale, high-frequency components.
 Decompositions are done on higher levels, lower frequency
Decompositions are done on higher levels, lower frequency
components are filtered out progressively
components are filtered out progressively
Fig-1

6. 6
6
Use of DWT in this work
Use of DWT in this work
 In this study, the line transient signals are used as the input
In this study, the line transient signals are used as the input
signals of the wavelet analysis.
signals of the wavelet analysis.
 Daubechies db3 wavelet is used.
Daubechies db3 wavelet is used.
 The fault transients of the study cases are analyzed through
The fault transients of the study cases are analyzed through
discrete wavelet transform at the level three.
discrete wavelet transform at the level three.
 Both approximation and details information related fault
Both approximation and details information related fault
voltages are extracted from the original signal with the multi-
voltages are extracted from the original signal with the multi-
resolution analysis.
resolution analysis.

7. 7
7
Original time-domain signal for transient 3-phase
Original time-domain signal for transient 3-phase
fault under studied.
fault under studied.
Fig-3

8. 8
8
Fig. 6, Fig.7 and Fig. 8 show transient signals for SLG, L-L
Fig. 6, Fig.7 and Fig. 8 show transient signals for SLG, L-L
and 3-phase faults at 80 km distance respectively
and 3-phase faults at 80 km distance respectively
Voltage waveform for 3-phase fault
at 80 km distance
Voltage waveform for SLG fault at
80 km distance
Voltage waveform for L-L fault at
80 km distance
Fig-8
Fig-7
Fig-6

9. JU
JU 9
9
i) The iterations are based on a given set of input and output (target) pattern
i) The iterations are based on a given set of input and output (target) pattern
pairs, called the training sets.
pairs, called the training sets.
ii) Back-propagation algorithm.
ii) Back-propagation algorithm.
iii) ANN attempts to minimize error by adjusting each value of a network
iii) ANN attempts to minimize error by adjusting each value of a network
proportional to the derivative of error with respect to that value.
proportional to the derivative of error with respect to that value.
iv) In the back-propagation learning, the actual outputs are compared with the
iv) In the back-propagation learning, the actual outputs are compared with the
target values to derive the error signals, which are propagated backward layer
target values to derive the error signals, which are propagated backward layer
by layer for updating the synaptic weights in all the previous layers.
by layer for updating the synaptic weights in all the previous layers.
Neural Network Model
Neural Network Model
Fig- 9

10. 10
10
 The number of inputs is 3 and at the output, a range of target values were set as
The number of inputs is 3 and at the output, a range of target values were set as
fault indicator index.
fault indicator index.
 MATLAB is used for the entire operation.
MATLAB is used for the entire operation.
 45 training patterns were generated combining all the three fault types.
45 training patterns were generated combining all the three fault types.
 Total 33 testing (predicting) patterns were generated at fault distances different
Total 33 testing (predicting) patterns were generated at fault distances different
from those used for training after the training process was over.
from those used for training after the training process was over.

11. 11
11
Fault parameter values (in per unit scale) for single
Fault parameter values (in per unit scale) for single
line to ground fault, (A) maximum values (B)
line to ground fault, (A) maximum values (B)
minimum values (C) Predominant frequency
minimum values (C) Predominant frequency.
.

12. 12
12
Fault parameter values (in per unit scale) for line to
Fault parameter values (in per unit scale) for line to
line fault at different distances, (A) maximum values
line fault at different distances, (A) maximum values
(B) minimum values (C) Predominant frequency
(B) minimum values (C) Predominant frequency

13. 13
13
Fault parameter values (in per unit scale) for three
Fault parameter values (in per unit scale) for three
phase fault at different distances, (A) maximum values
phase fault at different distances, (A) maximum values
(B) minimum values (C) Predominant frequency.
(B) minimum values (C) Predominant frequency.

14. 14
14
BPN performance for different fault conditions
BPN performance for different fault conditions
Fault Distance
(Km)
Fault
Types
Range of Target Values
Predicted
Value
From To
20
LG 0.5 1.5
LL 19.5 20.5
PF 38.5 39.5 38.1588
30
LG 1.5 2.5
LL 20.5 21.5
PF 39.5 40.5 40.2936
40
LG 2.5 3.5
LL 21.5 22.5 21.9136
PF 40.5 41.5 41.0652
50
LG 3.5 4.5
LL 22.5 23.5 23.1589
PF 41.5 42.5
60
LG 4.5 5.5 5.068
LL 23.5 24.5 23.8451
PF 42.5 43.3

15. 15
15
THANK YOU
THANK YOU