Differential Protection for Power Transformer Using Wavelet Transform and PNN
A new approach for protection of power transformer is
presented using a time-frequency transform known as Wavelet transform.
Different operating conditions such as inrush, Normal, load,
External fault and internal fault current are sampled and processed
to obtain wavelet coefficients. Different Operating conditions provide
variation in wavelet coefficients. Features like energy and Standard
deviation are calculated using Parsevals theorem. These features
are used as inputs to PNN (Probabilistic neural network) for fault
classification. The proposed algorithm provides more accurate results
even in the presence of noise inputs and accurately identifies inrush
and fault currents. Overall classification accuracy of the proposed
method is found to be 96.45%. Simulation of the fault (with and
without noise) was done using MATLAB AND SIMULINK software
taking 2 cycles of data window (40 m sec) containing 800 samples.
The algorithm was evaluated by using 10 % Gaussian white noise.
[1] M.A. Rahman, and B.Jeyasurya, A state-of-the-art review of transformer
protection algorithms, IEEE.Trans. Power Delivery, vol.3, pp.534-544,
April.1988.
[2] T.S. Sidhu, M.S. Sachdev, H.C.Wood, M.Nagpal, Design, implementation
and testing of a micro-processor-based high-speed relay for detecting
transformer winding faults, IEEE.Trans. Power Deliv, vol.7, pp.108-
117,Januaury 1992.
[3] P.Bastard, M.Meunier, and H.Regal, Neural network-based algorithm for
power transformer differential relays, IET Generation Trans. & Distr.,
vol.142, pp.386-392,1995.
[4] P.Liu, O.P.Malik, C.Chen, G.S.Hope, and Y.Guo, Improved operation
of differential protection of power transformers for internal
faults,IEEE.Trans.Power Deliv., vol.7, pp.1912-1919, October 1992.
[5] M.Geethanjali, S.M.R. Slochanal, and R.Bhavani, PSO trained ANNbased
differential protection scheme for power transformers, Neurocomputing,
vol.71, pp.904-918, January 2008.
[6] D.V.Coury, and E.C. Segatto, An alternative approach using artificial
neural networks for power transformer protection, European Transaction
Electrical Power, vol.16, pp.63-67, October 2005.
[7] A.Wiszniewski, and Kasztenny, A multi-criteria differential transformer
relay based on fuzzy logic, IEEE.Trans.Power Deliv , vol. 10, pp. 1786-
1792, October 1995.
[8] M.C. Shin, C.W.Park, and J.H. Kim, Fuzzy logic-based relaying for large
power transformer protection, IEEE.Transaction on Power Delivery, vol.
18, pp. 718-713,July 2003.
[9] Franti sek Jan cek Martin Mucha Marian Ostrozlk, A New Protection
Relay Based On Fault Transient Analysis Using wavelet Transform,
Journal of Electrical Engineering, vol.58, no.3, 271278, 2007
[10] P.L.Mao, R.K.Aggarwal, A wavelet transform based decision making
logic method for discrimination between internal faults and inrush currents
in power transformers, Electric Power System Research, vol. 22,
pp. 389-395,June 2000.
[11] A.R. Sedighi, and M.R. Haghifam, Detection of inrush current in
distribution transformer using wavelet transform, Int. J. Power and Energy
System, vol .27, pp.361-370,July 2005.
[12] O.A.S.Youssef, A Wavelet-based technique for Discrimination between
faults and Magnetizing inrush currents in transformers, IEEE Transaction
on Power Delivery, vol .18, pp.170 -176, January 2003.
[13] A.Saleh, and M.A.Rahman, Modeling and protection of a three phase
power transformer using wavelet packet transform, IEEE Transaction on
Power Delivery, vol.20, pp.1273-1282,April 2005.
[14] A.Zabardast, M.R. Feyzi, and A.Mehraein, Distinction between Inrush
current of Power Transformer and fault Currents in differential protection
using wavelet transformer, IEEE International conference on deregulation
and Restructuring and Power Technologies, Nanjing, China, p.1-6,2008.
[15] A.I. Megahed, A.Ramadan, and W.E.L. Mahdy, Power transformer
differential relay using wavelet transform energies, IEEE Power and
Energy Society General Meeting, Pittsburgh, USA, pp.1-6,2008.
[16] P.L.Mao, and R.K.Aggarwal, A novel approach to the classification of
the transient phenomena in power transformers using combined wavelet
transform and neural network, IEEE.Transaction on Power Delivery,
vol.16, pp. 654-660,October 2001.
[17] S.Sudha, and A.E.Jeyakmar, Wavelet and ANN based relaying for power
transformer protection, Journal of computer science, vol. 3, pp. 454-460,
June 2007.
[18] S.Jazebi, B.Vahidi, S.H.Hosseinian, and J.Faiz, Magnetizing inrush
current identification using wavelet based Gaussian mixture models,
Simulation modelling Practice and Theory, vol. 17, pp. 991-1010, July
2009.
[19] H.Monsef, and S.Lotfifard, Internal fault current identification based
on wavelet transform in power transformers, Electric Power System
Research, vol. 77, pp. 1637-1645, October 2007.
[20] Okan Ozgonenel, Wavelet based ANN approach for Transformer Protection,
Int.J. Comp., Intel., vol.2, pp. 161-168,2006.
[21] S.R.Samantaray, B.K.Panigrahi, P.K.Dash, and G.Panda, Power transformer
protection using S-transform with complex window and pattern
recognition approach, IET Generation Trans. & Distr, vol.1,pp. 278-286,
March 2007.
[22] Faiz,Jawad and Lotfi-Fard, A Novel Wavelet-Based Algorithm for Discrimination
of Internal Faults From Magnetizing Inrush Currents in Power
Transformers, IEEE Trans. Power Del.,vol.21,pp. 1989-1996,October
2006.
[23] Zwe-Lee Gaing,, Wavelet-based neural network for power disturbance
recognition and classification, IEEE Transaction on Power Delivery,
Vol.19, pp.1560-1568, October 2004.
[24] S. Valsan, K. Swarup, Wavelet transform based digital protection for
transmission lines, International Journal of Electrical Power & Energy
Systems, Volume 31, Issue 7, ,Pages 379-388, Sep 2009
[25] C.Sarathi, V.Sukanaya, and Y.Narasimha Murthy, ECG Signal Analysis
Using Wavelet Transforms, Bulg.J. Phys, vol.35, pp-68-77, 2008
[26] Jain-Da Wu and Chiu-Hong Liu, An automotive generator fault diagnosis
system using discrete wavelet transform and artificial neural
network,Expert Systems with Applications, vol. 36, pp 9776-9783, Aug
2009.
[27] Jain-Da Wu and Chiu-Hong Liu, Investigation of engine fault diagnosis
using discrete wavelet transform and neural network, Expert Systems with
Applications, vol 35, pp 1200-1213, 2008
[28] D.F.Specht, Probabilistic neural networks, Neural Networks vol.3,pp.
109-118,1990.
[1] M.A. Rahman, and B.Jeyasurya, A state-of-the-art review of transformer
protection algorithms, IEEE.Trans. Power Delivery, vol.3, pp.534-544,
April.1988.
[2] T.S. Sidhu, M.S. Sachdev, H.C.Wood, M.Nagpal, Design, implementation
and testing of a micro-processor-based high-speed relay for detecting
transformer winding faults, IEEE.Trans. Power Deliv, vol.7, pp.108-
117,Januaury 1992.
[3] P.Bastard, M.Meunier, and H.Regal, Neural network-based algorithm for
power transformer differential relays, IET Generation Trans. & Distr.,
vol.142, pp.386-392,1995.
[4] P.Liu, O.P.Malik, C.Chen, G.S.Hope, and Y.Guo, Improved operation
of differential protection of power transformers for internal
faults,IEEE.Trans.Power Deliv., vol.7, pp.1912-1919, October 1992.
[5] M.Geethanjali, S.M.R. Slochanal, and R.Bhavani, PSO trained ANNbased
differential protection scheme for power transformers, Neurocomputing,
vol.71, pp.904-918, January 2008.
[6] D.V.Coury, and E.C. Segatto, An alternative approach using artificial
neural networks for power transformer protection, European Transaction
Electrical Power, vol.16, pp.63-67, October 2005.
[7] A.Wiszniewski, and Kasztenny, A multi-criteria differential transformer
relay based on fuzzy logic, IEEE.Trans.Power Deliv , vol. 10, pp. 1786-
1792, October 1995.
[8] M.C. Shin, C.W.Park, and J.H. Kim, Fuzzy logic-based relaying for large
power transformer protection, IEEE.Transaction on Power Delivery, vol.
18, pp. 718-713,July 2003.
[9] Franti sek Jan cek Martin Mucha Marian Ostrozlk, A New Protection
Relay Based On Fault Transient Analysis Using wavelet Transform,
Journal of Electrical Engineering, vol.58, no.3, 271278, 2007
[10] P.L.Mao, R.K.Aggarwal, A wavelet transform based decision making
logic method for discrimination between internal faults and inrush currents
in power transformers, Electric Power System Research, vol. 22,
pp. 389-395,June 2000.
[11] A.R. Sedighi, and M.R. Haghifam, Detection of inrush current in
distribution transformer using wavelet transform, Int. J. Power and Energy
System, vol .27, pp.361-370,July 2005.
[12] O.A.S.Youssef, A Wavelet-based technique for Discrimination between
faults and Magnetizing inrush currents in transformers, IEEE Transaction
on Power Delivery, vol .18, pp.170 -176, January 2003.
[13] A.Saleh, and M.A.Rahman, Modeling and protection of a three phase
power transformer using wavelet packet transform, IEEE Transaction on
Power Delivery, vol.20, pp.1273-1282,April 2005.
[14] A.Zabardast, M.R. Feyzi, and A.Mehraein, Distinction between Inrush
current of Power Transformer and fault Currents in differential protection
using wavelet transformer, IEEE International conference on deregulation
and Restructuring and Power Technologies, Nanjing, China, p.1-6,2008.
[15] A.I. Megahed, A.Ramadan, and W.E.L. Mahdy, Power transformer
differential relay using wavelet transform energies, IEEE Power and
Energy Society General Meeting, Pittsburgh, USA, pp.1-6,2008.
[16] P.L.Mao, and R.K.Aggarwal, A novel approach to the classification of
the transient phenomena in power transformers using combined wavelet
transform and neural network, IEEE.Transaction on Power Delivery,
vol.16, pp. 654-660,October 2001.
[17] S.Sudha, and A.E.Jeyakmar, Wavelet and ANN based relaying for power
transformer protection, Journal of computer science, vol. 3, pp. 454-460,
June 2007.
[18] S.Jazebi, B.Vahidi, S.H.Hosseinian, and J.Faiz, Magnetizing inrush
current identification using wavelet based Gaussian mixture models,
Simulation modelling Practice and Theory, vol. 17, pp. 991-1010, July
2009.
[19] H.Monsef, and S.Lotfifard, Internal fault current identification based
on wavelet transform in power transformers, Electric Power System
Research, vol. 77, pp. 1637-1645, October 2007.
[20] Okan Ozgonenel, Wavelet based ANN approach for Transformer Protection,
Int.J. Comp., Intel., vol.2, pp. 161-168,2006.
[21] S.R.Samantaray, B.K.Panigrahi, P.K.Dash, and G.Panda, Power transformer
protection using S-transform with complex window and pattern
recognition approach, IET Generation Trans. & Distr, vol.1,pp. 278-286,
March 2007.
[22] Faiz,Jawad and Lotfi-Fard, A Novel Wavelet-Based Algorithm for Discrimination
of Internal Faults From Magnetizing Inrush Currents in Power
Transformers, IEEE Trans. Power Del.,vol.21,pp. 1989-1996,October
2006.
[23] Zwe-Lee Gaing,, Wavelet-based neural network for power disturbance
recognition and classification, IEEE Transaction on Power Delivery,
Vol.19, pp.1560-1568, October 2004.
[24] S. Valsan, K. Swarup, Wavelet transform based digital protection for
transmission lines, International Journal of Electrical Power & Energy
Systems, Volume 31, Issue 7, ,Pages 379-388, Sep 2009
[25] C.Sarathi, V.Sukanaya, and Y.Narasimha Murthy, ECG Signal Analysis
Using Wavelet Transforms, Bulg.J. Phys, vol.35, pp-68-77, 2008
[26] Jain-Da Wu and Chiu-Hong Liu, An automotive generator fault diagnosis
system using discrete wavelet transform and artificial neural
network,Expert Systems with Applications, vol. 36, pp 9776-9783, Aug
2009.
[27] Jain-Da Wu and Chiu-Hong Liu, Investigation of engine fault diagnosis
using discrete wavelet transform and neural network, Expert Systems with
Applications, vol 35, pp 1200-1213, 2008
[28] D.F.Specht, Probabilistic neural networks, Neural Networks vol.3,pp.
109-118,1990.
@article{"International Journal of Electrical, Electronic and Communication Sciences:58509", author = "S. Sendilkumar and B. L. Mathur and Joseph Henry", title = "Differential Protection for Power Transformer Using Wavelet Transform and PNN", abstract = "A new approach for protection of power transformer is
presented using a time-frequency transform known as Wavelet transform.
Different operating conditions such as inrush, Normal, load,
External fault and internal fault current are sampled and processed
to obtain wavelet coefficients. Different Operating conditions provide
variation in wavelet coefficients. Features like energy and Standard
deviation are calculated using Parsevals theorem. These features
are used as inputs to PNN (Probabilistic neural network) for fault
classification. The proposed algorithm provides more accurate results
even in the presence of noise inputs and accurately identifies inrush
and fault currents. Overall classification accuracy of the proposed
method is found to be 96.45%. Simulation of the fault (with and
without noise) was done using MATLAB AND SIMULINK software
taking 2 cycles of data window (40 m sec) containing 800 samples.
The algorithm was evaluated by using 10 % Gaussian white noise.", keywords = "Power Transformer, differential Protection, internalfault, inrush current, Wavelet Energy, Db9.", volume = "4", number = "3", pages = "574-7", }
