This paper deals with the unsymmetrical fault detection in a standalone wind energy conversion system, monitoring the discrete wavelet transform coefficient rms values for source side and load side currents of the network. The acquired current signature of the load and source sides of the network at normal and at fault has been analyzed using Multi-resolution analysis of discrete wavelet transform considering Daubechies 20 mother wavelet, wherein five levels of decomposition have been tracked to detect the smallest frequency content in the signals. The rms,skwness and kurtosis of various coefficient value has been calculated and depending on the changes in the respective values, a feature has been extracted for exact prediction of various unsymmetrical faults in the network. Firstly this analysis has been done in MATLAB frame and thereafter, a real time model has been developed to authenticate the proposed fault diagnosis technique. The developed model has been done to generate DC voltage of around 2V, which has been inverted in MATLAB frame to compute the real time validation. Rule sets have been developed for exact prediction of various unsymmetrical faults in the system for simulated as well as practical case studies. Both the results has been seen to give satisfactory outcome and a comparative study has been provided in this section of work.

1. Presentation
On
Fault Identification in a stand-alone wind energy conversion system
using MRA of DWT , Skewness, Kurtosis and RMS values analysis
Submitted by
Rajdeep Haldar(EE/13/007)
Sambuddha Ghoshal(EE/13/028)
Pranab Paul(EE/13/029)
Sourav Sadhukhan(EE/13/035)
Gourab Sarkar(EE/13/036)
Under the guidance of
Mrs. Debopoma Kar Ray
Electrical Engineering Department
MCKV Institute of Engineering, Liluah, Howrah
02/06/2017 M.C.K.V.I.E. 1

2. Acknowledgments
We express our sincere gratitude to our college MCKV Institute of Engineering, our
EE department, for providing us an opportunity to undertake and complete such an interesting
project report.
We are very thankful to our respected teacher Prof. (Dr.) Arghya Sarkar , Head of
the department of Electrical Engineering, MCKV Institute of Engineering for his experienced
guidance, perseverance and hospitality.
We are very grateful to our respected guide Ms. Debopoma Kar Ray (Basak), who
was our guide throughout this project. We would not have completed this project within a short
period of time without her invigoration and support.
The project is a result of her teaching encouragement and offering her valuable time
for recording the data and completion of the project. We would also like to thank all the faculty
members and laboratory instructors of the Department of Electrical Engineering.
We are also great full to our Sir, Mr. Arijit Sen of Electronics and Communication
Engineering Department, for helping us in data acquisition, in our experimental validation work.
Without his help and guidance it would have been difficult to complete the study.
02/06/2017 M.C.K.V.I.E. 2

3. Abstract
• This paper deals with the unsymmetrical fault detection in a standalone wind
energy conversion system, monitoring the discrete wavelet transform coefficient
rms values for source side and load side currents of the network.
• The acquired current signature of the load and source sides of the network at
normal and at fault has been analyzed using Multi-resolution analysis of discrete
wavelet transform considering Daubechies 20 mother wavelet, wherein five levels
of decomposition have been tracked to detect the smallest frequency content in the
signals.
• The rms,skwness and kurtosis of various coefficient value has been calculated and
depending on the changes in the respective values, a feature has been extracted
for exact prediction of various unsymmetrical faults in the network.
• Firstly this analysis has been done in MATLAB frame and thereafter, a real time
model has been developed to authenticate the proposed fault diagnosis technique.
• The developed model has been done to generate DC voltage of around 2V, which
has been inverted in MATLAB frame to compute the real time validation.
• Rule sets have been developed for exact prediction of various unsymmetrical faults
in the system for simulated as well as practical case studies.
• Both the results has been seen to give satisfactory outcome and a comparative
study has been provided in this section of work.
02/06/2017 M.C.K.V.I.E. 3

4. System under Study
02/06/2017 M.C.K.V.I.E. 4
Figure 1. Block diagram of Standalone WECS under study
Circuit Diagram

5. Theoretical Background
In Discrete Wavelet Transform (DWT) the scale and translation variables are discretised but not
the independent variable of the original signal.
A DWT gives a number of wavelet coefficients depending upon the integer number of the
discretisation step in scale and translation, denoted by ‘m’ and ‘n’. So any wavelet coefficient can
be described by two integers, ‘m’ and ‘n’. If ‘a0’ and ‘b0’ are the segmentation step sizes for the
scale and translation, respectively, the scale and translation in terms of these parameters will be
a= a0
m
and b= nb0 a0
m
In terms of the new parameters ao, bo ,m,n becomes:-
and the discrete wavelet coefficients are given by
Using the multi-resolution Analysis (MRA) technique implements the decomposition of a signal
into its high frequency and low frequency components, which are collectively known as high pass
and low pass filters of Multi-Resolution Analysis respectively.
In this analysis the rms values of the approximation and detailed coefficients obtained from MRA
of DWT has been calculated and depending on the signature of the respective level rms value
deviation from normal, different unsymmetrical faults in the network has been assessed.
02/06/2017 M.C.K.V.I.E. 5

6. Wavelet decomposition and coefficient values for healthy condition
02/06/2017M.C.K.V.I.E.
6
Source side
Load side

7. Wavelet decomposition and coefficient values for LG fault condition at source side
02/06/2017 M.C.K.V.I.E. 7
Source side
Load side

8. Wavelet decomposition and coefficient values for LLG fault condition at source side
02/06/2017 M.C.K.V.I.E. 8
Source side
Load side

9. Wavelet decomposition and coefficient values for LL fault condition at source side
02/06/2017 M.C.K.V.I.E. 9
Source side
Load side

10. Wavelet decomposition and coefficient values for LG fault condition at load side
02/06/2017 M.C.K.V.I.E. 10

11. Wavelet decomposition and coefficient values for LL fault condition at load side
02/06/2017 M.C.K.V.I.E. 11

12. Wavelet decomposition and coefficient values for LLG fault condition at load side
02/06/2017 M.C.K.V.I.E. 12

13. Source side current RMS Analysis Of Wavelet Coefficients at normal
02/06/2017 M.C.K.V.I.E. 13
Decomposition levels Coefficients RMS values
1 Approximate
378.0314
Detailed
5.669169
2 Approximate
537.6664
Detailed
11.52989
3 Approximate
805.4486
Detailed
34.32554
4 Approximate
1109.597
Detailed
197.8204
5 Approximate
2022.742
Detailed
450.523

14. Load side current RMS Analysis Of Wavelet Coefficients at normal
02/06/2017 M.C.K.V.I.E. 14
Decomposition levels Coefficients RMS values
1 Approximate
128.1979
Detailed
2.101919
2 Approximate
182.3862
Detailed
8.048519
3 Approximate
257.159
Detailed
17.31781
4 Approximate
362.5549
Detailed
35.65532
5 Approximate
543.5693
Detailed
225.9481

15. Source side current RMS Analysis Of Wavelet Coefficients at LL, LG, LLG faults in generator b
02/06/2017 M.C.K.V.I.E. 15
Level Coefficients RMS values
LL fault LG fault LLG fault
1 Approximate
639.78 495.07 770.13
Detailed
6.6085 11.305 7.9894
2 Approximate
908.73 716.63 1086.1
Detailed
17.474 41.641 12.344
3 Approximate
1287.4 1028.6 1508.8
Detailed
27.501 82.212 37.247
4 Approximate
1910.9 1505.8 2150.3
Detailed
109.77 271.61 168.27
5 Approximate
2688.8 2379.1 2852.1
Detailed
323.01 510.08 938.23

16. Load side current RMS Analysis Of Wavelet Coefficients at LL, LG, LLG faults in generator bus
02/06/2017
M.C.K.V.I.E.
16
Level Coefficients RMS values
LL fault LG fault LLG fault
1 Approximate
131.32 229.86 132.11
Detailed
1.7769 5.0552 1.7777
2 Approximate
185.48 327.21 186.66
Detailed
5.8447 7.0361 8.3671
3 Approximate
262.57 471.77 266.62
Detailed
14.218 19.327 17.073
4 Approximate
365.94 654.47 371.33
Detailed
28.337 74.279 35.718
5 Approximate
525.73 1146.1 522.74
Detailed
138.13 398.13 91.353

17. Feature Extractions For LL, LG And LLG Fault Identification ( Generator
side)
Feature extraction for generator bus fault identification from
rms value analysis of approximation coefficients (source
side)
Feature extraction for generator bus fault identification from
rms value analysis of detailed coefficients (source side)
Feature extraction for generator bus fault identification from
rms value analysis of approximation coefficients (load side)
Feature extraction for generator bus identification from rms
value analysis of detailed coefficients (load side)
02/06/2017 M.C.K.V.I.E. 17

18. 02/06/2017 M.C.K.V.I.E. 18
Level Coefficients RMS values
LL fault LG fault LLG fault
1 Approximate 617.5169 453.6137 713.729
Detailed 5.307116 5.324153 5.907887
2 Approximate 873.7853 643.4849 1010.203
Detailed 9.281542 19.64083 11.05147
3 Approximate 1239.874 914.4101 1438.733
Detailed 28.676 36.8248 31.82275
4 Approximate 1722.284 1270.117 2254.187
Detailed 105.6129 93.67834 98.16937
5 Approximate 2347.389 1789.776 3042.821
Detailed 434.3297 516.9374 521.3671
RMS values of source side current spectrums for LL, LG, LLG faults at load side

19. 02/06/2017 M.C.K.V.I.E. 19
Level Coefficients RMS values
LL fault LG fault LLG fault
1 Approximate 181.774 127.1131 713.729
Detailed 1.809429 1.743208 5.907887
2 Approximate 256.0097 179.0316 1010.203
Detailed 5.007761 2.960142 11.05147
3 Approximate 364.2376 254.4283 1438.733
Detailed 15.50121 9.557573 31.82275
4 Approximate 513.7587 358.9238 2254.187
Detailed 24.06067 25.65742 98.16937
5 Approximate 717.1517 568.9945 3042.821
Detailed 199.9688 83.16122 521.3671
RMS values of load side current spectrums for LL, LG, LLG faults at load side

20. 02/06/2017 M.C.K.V.I.E. 20
Feature Extractions For LL, LG And LLG Fault Identification (Load side bus)
Feature extraction for load bus fault identification from rms
value analysis of approximation coefficients (source side)
Feature extraction for load bus fault identification from
rms value analysis of details coefficients ( source side)
Feature extraction for load bus fault identification from rms
value analysis of approximate coefficients (load side)
Feature extraction for load bus fault identification from
rms value analysis of approximate coefficients (load side)

21. 02/06/2017 M.C.K.V.I.E. 21
Source side current Skewness Analysis Of Wavelet Coefficients at normal
Decomposition levels Coefficients Skewness values
1 Approximate -0.01339
Detailed -0.17514
2 Approximate -0.03295
Detailed -1.32368
3 Approximate -0.06995
Detailed 0.242319
4 Approximate -0.14407
Detailed 0.171094
5 Approximate -0.31513
Detailed -0.10621

22. 02/06/2017 M.C.K.V.I.E. 22
Load side current Skewness Analysis Of Wavelet Coefficients at normal
Decomposition levels Coefficients Skewness values
1 Approximate -0.00145
Detailed -0.6324
2 Approximate 0.001528
Detailed 2.518314
3 Approximate 0.007446
Detailed 0.716615
4 Approximate 0.020698
Detailed 1.285346
5 Approximate 0.04426
Detailed -0.07544

23. 02/06/2017 M.C.K.V.I.E. 23
Source side current Kurtosis Analysis Of Wavelet Coefficients at normal
Decomposition levels Coefficients Kurtosis values
1 Approximate 1.495922
Detailed 99.79288
2 Approximate 1.492209
Detailed 56.12955
3 Approximate 1.489988
Detailed 35.51113
4 Approximate 1.486702
Detailed 24.42911
5 Approximate 1.580086
Detailed 3.469751

24. 02/06/2017 M.C.K.V.I.E. 24
Load side current Kurtosis Analysis Of Wavelet Coefficients at normal
Decomposition levels Coefficients Kurtosis values
1 Approximate 1.502547
Detailed 103.3608
2 Approximate 1.504211
Detailed 135.469
3 Approximate 1.506871
Detailed 99.88604
4 Approximate 1.50683
Detailed 35.7106
5 Approximate 1.543202
Detailed 6.345082

25. 02/06/2017 M.C.K.V.I.E. 25
Level Coefficients Skewness values
LL fault LG fault LLG fault
1 Approximate 0.00891 0.004051 0.002516
Detailed -0.17975 -0.31478 0.038965
2 Approximate 0.019178 0.013732 0.002713
Detailed 0.016043 0.887511 -0.13023
3 Approximate 0.03673 0.032777 0.00319
Detailed 0.170514 0.445972 0.264675
4 Approximate 0.061719 0.072961 0.005379
Detailed 0.625364 -0.02592 0.086461
5 Approximate 0.083803 0.119334 -0.00538
Detailed -0.55682 -0.00456 -0.18838
Source side Skewness Analysis Of Wavelet Coefficients at LL, LG, LLG faults in
source side

26. 02/06/2017 M.C.K.V.I.E. 26
Level Coefficients Skewness values
LL fault LG fault LLG fault
1 Approximate 0.002624 0.019569 0.003112
Detailed 0.072079 -0.05502 0.010786
2 Approximate 0.006199 0.025333 0.008699
Detailed -0.42629 -0.0356 0.375878
3 Approximate 0.013287 0.036437 0.01975
Detailed -0.60291 0.104321 0.24549
4 Approximate 0.02629 0.044845 0.041983
Detailed -0.61312 -0.03039 0.063178
5 Approximate 0.061009 0.112922 0.084355
Detailed -0.18912 -0.01862 0.360648
Load side Skewness Analysis Of Wavelet Coefficients at LL, LG, LLG
faults in source side

27. 02/06/2017 M.C.K.V.I.E. 27
Level Coefficients Kurtosis values
LL fault LG fault LLG fault
1 Approximate 1.731736 1.498862 1.606521
Detailed 17.57612 15.29745 15.6858
2 Approximate 1.754151 1.502256 1.60732
Detailed 10.52569 35.58154 7.428806
3 Approximate 1.791537 1.546337 1.608419
Detailed 8.758571 20.73361 9.356144
4 Approximate 1.840528 1.54543 1.607178
Detailed 42.78958 2.373368 53.28107
5 Approximate 1.862598 1.529321 1.61077
Detailed 54.79014 3.051837 42.75261
Source side Kurtosis Analysis Of Wavelet Coefficients at LL, LG, LLG
faults in source side

28. 02/06/2017 M.C.K.V.I.E. 28
Level Coefficients Kurtosis values
LL fault LG fault LLG fault
1 Approximate 1.501338 1.587121 1.499355
Detailed 16.99945 9.0657 15.34421
2 Approximate 1.503469 1.588903 1.498318
Detailed 20.89447 6.804406 17.91369
3 Approximate 1.507933 1.602708 1.496731
Detailed 27.79417 5.568114 27.4928
4 Approximate 1.517592 1.50174 1.493616
Detailed 45.8024 2.908291 41.66792
5 Approximate 1.532673 1.589668 1.489268
Detailed 39.29482 5.139542 46.23959
Load side Kurtosis Analysis Of Wavelet Coefficients at LL, LG, LLG faults
in source side

29. 02/06/2017 M.C.K.V.I.E. 29
Feature extractions for LL, LG and LLG fault at source side identification using
Skewness and Kurtosis value analysis for source side current.

30. 02/06/2017 M.C.K.V.I.E. 30
Feature extractions for LL, LG and LLG fault at source side identification using
Skewness and Kurtosis value analysis for load side current.

31. 02/06/2017 M.C.K.V.I.E. 31
Level Coefficients Skewness values
LL fault LG fault LLG fault
1 Approximate -0.03514 -0.00221 0.016009
Detailed -0.13522 0.226096 4.371866
2 Approximate -0.07652 -0.005 0.034012
Detailed -0.73658 0.276028 -1.76333
3 Approximate -0.14812 -0.01059 0.06608
Detailed -0.42996 0.170794 0.003883
4 Approximate -0.25532 -0.01734 0.117219
Detailed -0.51682 0.60522 0.093933
5 Approximate -0.35902 -0.05017 0.163501
Detailed -0.35902 -0.07076 0.125944
Source side Skewness Analysis Of Wavelet Coefficients at LL, LG, LLG
faults in load side

32. 02/06/2017 M.C.K.V.I.E. 32
Level Coefficients Skewness values
LL fault LG fault LLG fault
1 Approximate 0.001318 0.002851 0.016009
Detailed -0.28492 -0.01353 4.371866
2 Approximate 4.29E-05 0.00752 0.034012
Detailed -2.78988 -0.07094 -1.76333
3 Approximate -0.00244 0.016793 0.06608
Detailed 0.243855 -0.09026 0.003883
4 Approximate -0.0078 0.035813 0.117219
Detailed -1.20755 -0.05297 0.093933
5 Approximate -0.02874 0.074567 0.163501
Detailed 0.141903 -0.29069 0.125944
Load side Skewness Analysis Of Wavelet Coefficients at LL, LG, LLG
faults in load side

33. 02/06/2017 M.C.K.V.I.E. 33
Level Coefficients Kurtosis values
LL fault LG fault LLG fault
1 Approximate 1.847466 1.973001 1.660812
Detailed 225.1158 16.09459 287.9464
2 Approximate 1.955504 1.972289 1.708085
Detailed 121.8549 11.87682 118.3351
3 Approximate 2.131499 1.9694 1.789696
Detailed 76.75545 16.54555 80.40759
4 Approximate 2.369899 1.960292 1.922053
Detailed 72.38661 40.44648 60.74021
5 Approximate 2.496105 1.972842 1.992691
Detailed 70.70538 44.31766 55.86645
Source side Kurtosis Analysis Of Wavelet Coefficients at LL, LG, LLG
faults in load side

34. 02/06/2017 M.C.K.V.I.E. 34
Level Coefficients Kurtosis values
LL fault LG fault LLG fault
1 Approximate 1.501157 1.496464 1.660812
Detailed 119.2922 15.34837 287.9464
2 Approximate 1.502868 1.495122 1.708085
Detailed 199.5778 6.461887 118.3351
3 Approximate 1.506193 1.49299 1.789696
Detailed 95.43031 9.549587 80.40759
4 Approximate 1.51208 1.496044 1.922053
Detailed 46.56893 30.60578 60.74021
5 Approximate 1.540432 1.494198 1.992691
Detailed 40.20908 43.0236 55.86645
Load side Kurtosis Analysis Of Wavelet Coefficients at LL, LG, LLG faults
in load side

35. 02/06/2017 M.C.K.V.I.E. 35
Feature extractions for LL, LG and LLG fault at load side identification using
Skewness and Kurtosis value analysis for source side current.

36. 02/06/2017 M.C.K.V.I.E. 36
Feature extractions for LL, LG and LLG fault at load side identification using
Skewness and Kurtosis value analysis for load side current.

37. 02/06/2017 M.C.K.V.I.E. 37
Practical validation of the proposed fault diagnosis technique
Experimental setup

38. 02/06/2017 M.C.K.V.I.E. 38
Practical validation of the proposed fault diagnosis technique
Equipments Specification
DC Generator 12V, 15W, No-load speed: 3600 rpm,, No load current: 1A, Nominal
speed: 2700rpm, Nominal torque: 44N-m, Nominal current-2A, Stall
torque: 167 N-m, Length: 2.5 cm, Breadth: 1.5 cm, Height: 1.5 cm.
Turbine Material-PVC, Dimension-2.5 cm x 2.5cm.
LED light 1V
Digital Storage Oscilloscope Maker’s Name- Tektronix, Serial No.- TDS 1002, Rating-
60MHz, 1GS/s.
Tower Stainless steel rods, tilted at an angle of 30°, Height-21 cm.
Road Length: 6.5 cm, Breadth: 13.5 cm.
Street light post Height: 7.5 cm.
Wooden base Length: 19 cm, Breadth: 13.5 cm, Height: 0.5 inch.
Pulley Material: PVC, Diameter: 7cm.
Specification of equipment used

39. 02/06/2017 M.C.K.V.I.E. 39
AC voltage spectrum of practical wind turbine at Normal condition
DWT decomposition levels of system voltage at Normal
condition
Approximate and Detailed coefficients of DWT decomposition
levels of system voltage at Normal condition
Voltage signal, MRA of DWT frame and decomposition levels at normal

40. 02/06/2017 M.C.K.V.I.E. 40
Voltage signal, MRA of DWT frame and decomposition levels at short in
DC generator
Inverted Voltage spectrum of practical wind turbine at short
in DC motor
DWT decomposition levels of system voltage at short
Approximate and Detailed coefficients of DWT
decomposition levels of system voltage at short

41. 02/06/2017 M.C.K.V.I.E. 41
Level Coefficients Skewness Kurtosis RMS
1 Approximate 0.393621 3.930524 1.70
Detailed -0.58321 3.808917 8.475
2 Approximate -0.80683 3.678599 1.114
Detailed 0.420975 3.844752 1.4614
3 Approximate -0.87576 4.127247 1.3314
Detailed -0.07309 3.029964 9.5715
4 Approximate -1.43332 4.492825 1.1814
Detailed -0.46066 2.658646 4.4415
5 Approximate -2.29726 6.870627 1.0114
Detailed -0.84104 5.187664 1.6614
Skewness, Kurtosis and RMS analysis of DWT decomposition levels at normal

42. 02/06/2017 M.C.K.V.I.E. 42
Level Coefficients Skewness Kurtosis RMS
1 Approximate 0.012517 1.536081 0.158568
Detailed 0.016743 1.593354 0.072489
2 Approximate -0.74986 6.508456 0.057894
Detailed -0.06378 1.580917 0.239532
3 Approximate -0.68803 3.993251 0.029474
Detailed 0.022203 4.949542 0.079861
4 Approximate -0.48854 2.594005 0.0303
Detailed 0.316526 2.874483 0.040521
5 Approximate 0.07841 2.1000774 0.017336
Detailed 0.120536 3.316569 0.045587
Skewness, Kurtosis and RMS analysis of DWT decomposition levels at short in DC
generator

43. 02/06/2017 M.C.K.V.I.E. 43
DWT levels Coefficients Skewness Kurtosis RMS Percentage
deviation
1 Approximate 0 1 1 23.3%
Detailed 1 1 1
2 Approximate 0 1 1
Detailed 1 1 1
3 Approximate 0 1 1
Detailed 0 1 1
4 Approximate 0 1 1
Detailed 0 1 1
5 Approximate 0 1 1
Detailed 1 1 1
Deviation in simulated and practical values at normal condition

44. 02/06/2017 M.C.K.V.I.E. 44
DWT levels Coefficients Skewness Kurtosis RMS Percentage
deviation
1 Approximate 1 1 1 23.3%
Detailed 0 1 1
2 Approximate 0 1 1
Detailed 1 1 1
3 Approximate 0 1 1
Detailed 0 1 1
4 Approximate 0 1 1
Detailed 0 1 1
5 Approximate 1 1 1
Detailed 0 1 1
Deviation in simulated and practical values at fault condition

45. Conclusion
• This work presents an unsymmetrical fault identification and localization
technique for a standalone wind energy conversion system.
• In this analysis, monitoring the source side and load side current’s MRA of
DWT decomposition level rms values, at normal and LL, LG and LLG faults in the
generator bus of the network, the proposed technique has been executed.
• The feature extraction from the approximate and detailed coefficient rms values
of the respective decomposition levels clearly depict how the occurrence of LL,
LG and LLG faults in the system can be distinguished and how the exact
location of the fault can be identified, seeing the nature of the rms , skwness
and kurtosis signatures of the wavelet decomposition levels.
• This analysis has been firstly done in MATLAB frame and cross validation of the work
has been done developing a real time model of stand-alone WECS.
• Both the simulated and practical data has been seen to give satisfactory result, since
the percentage deviation from theoretical and practical analysis is only 23.3% for both
normal and fault conditions of the network.
02/06/2017 M.C.K.V.I.E. 45

46. Future Scope of the work
• The future scope of this analysis aspires to incorporate fault identification in Grid
interconnected WECS using the proposed fault diagnosis technique.
02/06/2017 M.C.K.V.I.E. 46

47. References
[1] D. P. Kothari, K. C. Singal, R. Ranjan, “Renewable Energy Sources and Emerging Technologies”, Prentice-Hall of India
Private Limited, New Delhi, 2008.
[2] P. D. Dunn, “Renewable Energy Sources, Conversion and Application”, Peter Peregrinus Limited, London, U.K.1986.
[3] J. Gimpel, “The Medieval Machines”, Pub. Gollancz, London, 1977.
[4] B. H. Khan, “Non-Conventional Energy Resources”, 2nd
Edition, Tata McGraw-Hill Education Private Limited, New Delhi,2009.
[5] D. Kar. Ray, S. Deb, T. Kumar, S. Sengupta, Diagnosis of Sub-synchronous Inter-harmonics in Power System Signals under
non-sinusoidal Environment, LCIT, National Journal of Engineering & Technology, Vol I, 2012, pp. 272-276.
[6] D. Kar. Ray, S. Deb, T. Kumar, S. Sengupta, Diagnosis of Sub-synchronous inter-harmonics in Power System Signals using
Multi-Resolution Analysis of Discrete Wavelet Transform, IEM, International Journal of Management and Technology,
August, 2012, Vol. 2, No. 2, ISSN No: 2229-6611, pp. 11-16.
[7] D. Kar. Ray, S. Deb, S. Sengupta, Diagnosis of Sub-synchronous Inter-Harmonics in Arc Furnace Transformer using Multi-
Resolution Analysis of Discrete Wavelet Transform, International Journal of Electrical, Electronics and Computer
Engineering 1(2) (2012), ISSN No. (Online): 2277-2626, pp. 66-70.
[8] D. Kar. Ray, S. Sengupta, Diagnosis of Unbalance in 3 phase Induction Motor using Multi-Resolution Analysis of Discrete
Wavelet Transform, International Journal of Electronics and Communication Technology, 2013, Vol. 4, Issue 1, ISSN:2230-
7109 (Online), 2230-9543 (Print), pp. 187-190.
[9] D. K. Ray, S. Chattopadhyay, K. D. Sharma, S. Sengupta, Assessment of Harmonic Voltage angles in a multi-bus power
system during symmetrical fault at certain bus, in Proc. MFIIS-2015, ISBN (print): 9789383701780, ISBN (online):
9789383701797, pp. 243-248.
[10] D. K. Ray, S. Chattopadhyay, K. D. Sharma, S. Sengupta, Steady State Harmonic Stability analysis in IEEE 14 bus system
for fault at generator bus, in Proc. MFIIS-2015, ISBN (print): 9789383701780, ISBN (online): 9789383701797, pp. 261-
266.
[11] D. K. Ray, S. Chattopadhyay, K. D. Sharma, S. Sengupta, Identification of Faulty Load Bus in a Multi-Bus Power system,
published in Proc. CIEC 16, pp.-289-293, ISSN: 9781509000357, held between 28th
-30th
January, 2016, at Applied Physics,
CU.
[12] A. Chattopadhyaya, H. Banerjee, S. Chattopadhyay, S. Sengupta, Assessment of CT Saturation caused by switching
transient, International Journal of Electrical, Electronics and Computer Engineering, 2(2), p.57-61, 2013.
[13] S. Chattopadhyay, A. Chattopadhyaya, S. Sengupta, Measurement of harmonic distortion and Skewness of stator current of
induction motor at crawling in Clarke Plane, IET Science, Measurement and Technology, ISSN: 1751-8822, 2014.
[14] S. Chattopadhyay, A. Chattopadhyaya, S. Sengupta, Analysis of Stator current of induction motor used in transport system at
single phasing by measuring phase angle, symmetrical components, Skewness, Kurtosis and harmonic distortion in Park
plane, IET Electrical systems in Transportation, ISSN: 2042-9738, 2013.
02/06/2017 M.C.K.V.I.E. 47

48. List of Publications
• D. K. Ray, G. Sarkar, P. Paul, R. Haldar, S. Ghoshal, S. Sadhukhan, S. Chattopadhyay,
"Unsymmetrical Fault detection in Wind energy conversion system using Multi-Resolution
analysis of Discrete Wavelet Transform", In ICAST-2017, ISBN: 978-1-945919-47-3, pp. 68-
73.
• D. K. Ray, G. Sarkar, P. Paul, R. Haldar, S. Ghoshal, S. Sadhukhan, S. Chattopadhyay,
"Generator and Load Bus Fault detection in Standalone WECS using MRA of DWT",
accepted in C+CA-Progress in Engineering Science (ISSN: 045-6152).
02/06/2017 M.C.K.V.I.E. 48

49. 02/06/2017 M.C.K.V.I.E. 49