The document discusses a method for discriminating between transformer inrush currents and internal faults using combined discrete Fourier transform (DFT) and artificial neural network (ANN) approaches. It highlights the challenges of false tripping in differential protection schemes during various operational conditions and proposes a new algorithm that uses second harmonic ratios to classify these currents reliably. Simulation results indicate that the ANN can accurately differentiate between inrush and fault currents, demonstrating high reliability in performance.

1. Academic Year 2018-19
By: Shilpa Mishra
Discrimination Between Transformer Inrush
Current and Internal Fault using Combined
DFT-ANN Approach
IIT JODHPUR

2. Introduction to Transformer Protection
 Modern power transformer : most vital element of EPS.
 Protection is critical : key issue is the false tripping of relay
due to such operating conditions for which tripping is not
required at all.
 Differential protection scheme: Most popular Transformer
Unit Protection method(conventional).
 How it works?

3. Cont.....
Under 3 cases-
1. Internal fault: Differential current in O.C.
initiation of trip signalC.B. isolates
Transformer. (NO issue except sensitivity)
2. External faults: Differential relay must not send
trip signal for this. (Issue arises during heavy
external fault leading false trip due to CTs
mismatch/ saturation)
3. Abnormal operating conditions: Differential
relay must not send trip signal for this. Ex.
Magnetising inrush current, over excitation,
online tap changing.

4. Magnetising Inrush Current?
 A high magnitude and of high frequency
transient current in primary of CT during start-
up of transformer causing a significant amount
of differential current in operating coil of relay
leading false trip.
 Inrush current is assumed to be rich in 2nd
harmonic compared to other harmonics;
exceeding 10-15% of fundament frequency
component present.
Note: internal fault is supposed to be rich in 3rd
and 5th (odd) harmonic components.

5. Motivation
 Difficulties: Differential Protection Scheme
 CTs Mismatch and saturation during heavy
external faults.
 Over-excitation of transformer
 Magnetizing inrush current during initial
energization.
 False tripping/ mal-operation of
transformer

6. How to overcome False Tripping ?
A. Percentage Biased Differential Scheme:
Applicable for initial 2 difficulties mentioned
above.
B. To Compensate for Inrush Induced Differential
Current:
i. Desensitize the relay during starting or add
time delay.
ii. Harmonic Restraint Relay: Designed to restrain
operation as long as the 2nd harmonic current
component exceeds 15% of the fundamental.
(characteristic of inrush current: presumed)

7.  To get reliable operation by HRR based differential
scheme several computer based digital signal
processing and pattern recognition techniques are
being used for inrush harmonic detection and
isolation; like-
 DFT and STFT
 FFT
 CWT and DWT
 Hilbert Transform
 ANN
 Fuzzy etc

8.  Fourier analysis based transform gives high
frequency resolution but zero time resolution
hence suitable for stationary signal.
 Solution is STFT: uses fixed size of time window
for all the frequencies, which restricts the
flexibility (High frequency resolution needs short
time window and vice-versa).
 Wavelet transform: a powerful tool for achieving
both time and frequency resolution with
adjustable and movable window : Multi-
Resolution Analysis (MRA) provided by WT.

9. Proposed Protection Algorithm
• SHR= I2/I1; Using Fourier
approach
• If the SHR> set value,
Output of ANN=0, No tripping
(Inrush current condition)
• If the SHR< set value,
Output of ANN=1,Tripping is
issued (internal fault condition)

10.  Here technique used to discriminate a magnetizing inrush
current from an internal-fault current is based on
combined DFT and ANN application.
 A full-cycle DFT is firstly applied as a preprocessing
module to extract distinctive features namely the
magnitudes of the fundamental and second harmonic
frequency components I1 and I2 respectively from
transient differential phase currents for each of the
phases a, b, and c.
 These features are then used to calculate the Second
Harmonic Ratio;
SHR= I2/I1
 Secondly, the three SHRs are fed to an ANN which is
already trained for classifying that transient phenomenon
into either magnetizing inrush or internal-fault current.
 The task of the ANN unit is to develop a block signal

11. Calculation of Second Harmonic Ratio using DFT
 Let the current waveform is sampled at N samples per
period of the fundamental(50Hz) denoted by ik . Here
N=20 is taken so fs = 1000Hz
 The real and imaginary parts of the nth harmonic current In
(an and bn) can be thus calculated as,
• The result are being updated iteratively each time a new sample
is available,
Here current sampling starts at rth
sample

12. NEURAL NETWORK TRAINING
 Here a 3-layer feed-forward ANN is considered for
this study
 The input layer has 3 neurons. The output layer has
one neuron which gives a binary output of 0 or 1.
 The number of neurons in hidden layers is problem-
based. (based on trade-off between the speed and
accuracy).

13. Cont...
 The process of determining the weights by
adapting its value iteratively to reduce the error Ep
is called training process.
 The Mean Squared Error (MSE) is used as a
performance criterion in this work.
 To train the proposed ANN a total of 1500
simulations of inrush and internal-fault currents
have been generated on-line as training and testing
patterns by MATLab simulation over a wide range
of inception angles α (0o-180o).

14.  To simulate inrush currents, the circuit breaker is
kept open-circuited, whereas to simulate internal
faults, the circuit breaker is kept closed.
MATLAB/Simulink schematic for 3-phase nonlinear transformer simulation with
DFT-ANN based protection scheme

15.  The ANN can be trained using Back propagation
(BP), Newton’s method, Steepest Descent (SD)
or LM algorithm with log-sigmoid transfer
functions in hidden and output layers.
 the LM algorithm is widely accepted as the most
efficient training algorithm in the sense of
realization accuracy among all.
 In this study, ANN has been trained using the LM
learning algorithm.

16. Results and Discussion
Training: 10 cycles of simulated inrush current, at α=0o and its
Harmonic spectrum

17. Training: 10 cycles of simulated inrush current, at α=90o and its
Harmonic spectrum

18. Training : 10 cycles of simulated 3-phase fault current, at α=0o and its
Harmonic spectrum

19. Training: 10 cycles of simulated 3-phase fault current, at α=90o and its
Harmonic spectrum

20.  These signals are used to extract SHR of the three
phases (SHRa, SHRb and SHRc) over a 10-cycle transient
current at 0o, 30o, 60o, 90o, 120o, 150o and 180o inception
angles as offline training data inputs.
(Phase
a)
Inrush current Internal Fault current

21. Phase c
Phase b

22. • From several tests it was found that an ANN of 3
neurons in the hidden layer is a good choice. It leads to an
MSE<1e-6 in 11 epochs.
• The ANN is trained off-line. Once the desired
performance is achieved, the weights of the ANN are
frozen.
MSE training convergence of the proposed (3-
3-1) ANN
Training performance: NN output
for actual and target patterns of
phase a

23. (Phase
a)
Inrush current Internal Fault current
• Testing performance of the ANN : 800 input-output
testing patterns representing inrush and internal-fault
transients at inception angles (15o, 45o, 75o, 105o, 135o
and 165o) are examined.

24. • The testing stage simulations illustrate the efficiency of the
proposed ANN. It can be seen that almost 100% of the 800 tested
inrush and fault patterns have been successfully classified.
(Phase
c)
(Phase b)(Phase
a)

25. Conclusion
 An ANN has been developed based on the SHR has for
inrush and internal-fault current discrimination.
 The ANN has been trained using LM algorithm to generate
a “no trip” (0) or “trip” (1) output signal according to the
values of the SHR that are extracted from the transient
differential current detected by the differential relay.
 The SHR has been calculated by DFT based harmonic
analysis.
 Performance of the given ANN-based discriminator is
reliable and very encouraging.
 Simulation results show that the proposed ANN is able to
classify current transients that have not been exposed to
during the training phase.

26. REFERENCES
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