This document discusses using artificial neural networks (ANNs) for fault detection and location in extra high voltage transmission lines. It presents a fault detector and locator trained on data from power system simulations of different fault scenarios. The fault detector identifies faults based on current and voltage signals. Three ANN-based fault locators are evaluated that use different inputs like current magnitudes, voltage and current magnitudes, and voltage magnitudes. Test results show the ANN approach can accurately detect and locate faults, with the best performance from the locator using both current and voltage phasor magnitudes. This neural network method provides high-speed fault protection for transmission lines.

1. FAULT LOCATION IN EHV
TRANSMISSION LINES USING
ARTIFICIAL
NEURAL NETWORKS
Presented by
K.SRAVANTHI
MTECH
ABDUL KALAM INSTITUTE OF
TECHNOLOGICAL
SCIENCES

2. ABSTRACT:
Application of ANNs to fault detection and location in EHV
transmission lines for high speed protection.
The proposed neural fault detector and locator were trained
using various sets of data available from a selected power
network model & simulating different
Fault scenarios:
Fault types, locations, resistances & inception angles.
&
Different power system data:
source capacities, voltages, angles, time constants of the
sources.

3. INTRODUCTION: FAULTS IN TRANSMISSION
LINES
Possible causes of faults
•LIGHTNING STRIKES
•SNOW STORMS OR ICING OF
LINES
•TREES MAY FALL

4. •POWER SYSTEM UNDER CONSIDERATION:
To evaluate the performance of the proposed neural
network-based fault detector and locator,
a 400 kV, 120 km transmission line extending between two
sources:

5. •MAJOR BLOCKS CONSTITUTING FD
& FL:
FD is designed to indicate the presence or absence of a fault.
 Fault is determined by the power system
state directly from instantaneous
FL is designed to estimate the distance of the fault in the line using
the phasor magnitude of
CURRENT
&
VOLTAGE
CURRENT
&
VOLTAGE

6. •FAULT DETECTOR:
Anti aliasing filter: to remove the unwanted frequencies
These signals are processed so as to simulate a 2 kHz sampling process.
Phas
e
curren
t
Phase
voltag
e
Zero
sequence
V&I

7. •STRUCTURE OF FAULT DETECTOR:
In order to construct a good neural network system, it is vitally
important to train & test it correctly.
ANN is trained with various i/p patterns corresponding to different
types of fault , fault conditions & power system data

8. •TEST RESULTS FOR FAULT DETECTOR:
for a b–g fault with Lf= 117 km,
Rf =5Ω & θf= 30◦, corresponds to the fault at t=27 ms.
For a a–c–g with Lf= 10 km,
Rf = 40 Ω and θf= 90◦,corresponds to the fault
at t= 30.5 ms.

9. For a b–c fault with Lf= 50 km, Rf = 0 Ω, & θf= 60◦,
corresponds to the fault at t=29 ms
For a a–b–c–g fault with Lf= 10 km, Rf = 80 Ω
& θf= 90◦, corresponds to the fault at t=30.5 ms

10. FAULT LOCATOR:
FL is activated when a fault is detected
by FD

11. STRUCTURE OF FAULT LOCATOR:
Three fault locators are presented in this:
FL1
FL2
FL3
CURRENT
MAGNITUDES
|Ia|, |Ib|, |Ic|
V&I
MAGNITUDES
|Va|, |Vb|, |Vc|
|Ia|, |Ib|, |Ic|
VOLTAGE
MAGNITUDES
|Va|, |Vb|, |Vc|

12. TEST RESULTS FOR FAULT LOCATOR:
The error in fault location is defined as
Error (km) = |ANN output − Fault location|

13. FL3 which uses current and voltage
phasor magnitudes, is the best fault locator. Representing
quite satisfactory results.

14. CONCLUSION
The test results of ANN demonstrates the effectiveness
&
the precision
of fault detection & fault location in a variety of
fault situations.
for high speed protection…….