Design, Development and Simulation of Front End Electronics for Nuclear Detec...
PPT_Final_Presentation
1. Detection and Classification
of Disturbances in a Hybrid
Distributed System Using
Wavelet Transform and ANN
GUIDE : Prof. P. R. Subadhra
By,
Sleeba Paul Puthenpurakel
2. Introduction
Rapid Increment in Energy Demand
India : By 2030, Demand = 6 x Current Demand
Feasibility of Renewable Energy Sources
Minimizes the environmental pollution
Advancement in Power Electronics, Internet of Things and Automation
7. Islanding Detection Methods
Communication Based
Communication between DG and Utility
Heavily relies on communication system
Reliable but complex and costly
Active
Perturb System Variables ( Voltage and Frequency)
Small Non - Detection Zone
8. Islanding Detection Methods
Passive
Measures system variables ( Voltage and Frequency )
No Power Quality Issues
Large non - detection zone
Selecting right variables is crucial
Threshold fixing is difficult
9. Computational Intelligence based methods
Mimics human intelligence
Solves non - linear multi objective problems
High speed and accuracy
Learn from examples
Training of algorithm is a one time process
Can detect and classify disturbances
10. Motivation of Thesis
Importance of Distributed Generation
Unintentional Islanding hazards
Inabilities of conventional methods
Power Quality issues
High Non – Detection zone
Cost of implementation
Popularity and Robustness of Computational Intelligence and Machine
learning
11. Objectives
Disturbance Detection using a conventional method in Hybrid System
Disturbance Detection using Wavelet Transform in Hybrid System
Disturbance Classification using Computational Intelligence Method
Comparative study
13. Hybrid System Specifications
PV System
Rated Power - 250 kW
Irradiance - 1000 W/m2
Wind Power Plant
Rated Power - 1.5 MW
Wind Speed - 12 m/s
Grid
14. Conventional Islanding Detection Method
Detection of Harmonics Method
THD
Measure of Harmonic Content in the signal
Based on Fourier Transform
Passive Method
THD of Voltage Signal at PCC is calculated
Normal value of THD at grid connected mode < 5% ( IEEE Standard)
15. Transform of a Signal
Real world signals are time domain ( Time V/s Amplitude )
Transform gives additional information
Frequency domain
Stationary signal
Frequency content dont change with time
All frequencies are present in all time !!
19. Fourier Transform - Anxieties
Gives the overall frequency content information
Frequency and Amplitude
Misses the time domain information
Can’t extract complete information from Non - Stationary signals
Frequency at a particular instant of time can’t be calculated
Can't differentiate events using the variation in frequency content of
20. THD (%) - Hybrid System Connected to Grid
Loading
(MW)
Grid
Connected
Islanding L-G Fault L-L Fault
Non -
Linear Load
Switch
0.875 1.2665 11.1161 3.5811 6.0342 62.3332
1 1.1347 8.0497 3.5966 6.0166 62.2358
1.3 0.953 3.3066 3.7453 5.9751 61.8576
1.75 0.7766 4.1653 3.9311 5.8795 61.3211
● Rated Power - 1.75 kW ( 0.25 MW + 1.5 MW )
● Loading varies from 50 % to 100 % of rated power
21. Inferences
THD can detect Islanding as well as Power Quality Issues
THD on Islanding event changes abruptly with load change
THD fails to maintain a threshold for Islanding
THD fails to differentiate Islanding and Power Quality Issues
23. Introduction to Wavelet Transform
Fourier Transform
Apt for Decomposition of Stationary
Signals
Basis function is Sine wave
Signal is represented as translated
and dilated versions of Sine
Wave
Frequency domain information is
available
Wavelet Transform
Apt for Decomposition of Non -
Stationary Signals
Basis function is Wavelet
Signal is represented as translated
and dilated versions of
Wavelet
Frequency and Time domain
information is available
24. Advanced Multirate Signal Processing
Non Stationary Signal
High Frequency Parts
Low Frequency Parts
High Frequency parts
Quick parts
Need more samples to detect them
Low Frequency parts
25. Discrete Wavelet Transform
v - Input Signal
Ψ - Mother Wavelet
- Translation Constant / Position
- Dilation Constant / Scale
Dyadic scale have =
26. Discrete Wavelet Transform - Working
● x[n] → Signal
● h[n] → HPF
● g[n] → LPF
Coefficients
Filters
Down - Sampling
27. Frequency Division
● Let sample frequency of signal = Fs
■ Eg. Fs = 1 kHz
Wavelet Level Frequency Band (Hz)
Level 1 500 - 1000
Level 2 250 - 500
Level 3 125 - 250
Level 4 62.5 - 125
28. Mother Wavelet Selection
Reconstruction capability
Empirically find if the input signal can be reconstructed by the wavelet perfectly
Similarity of Wavelet and Input Signal
29. Daubechies 4 Mother Wavelet
● Disturbance in power system features exhibit sharp changes
● Daubechies mother wavelet with low order
○ Which have an angular shape
○ Ideal to analyse sharp changes
■ For smooth features , a higher order is preferred.
31. Statistical Indices Of Wavelet Transform
At a particular frequency band / level
Standard Deviation
Power of signal when its mean =0
L2 Norm ( Energy )
32. Wavelet Transform Based Approach
Extract Neg.
Sequence Voltage
from PCC
Perform Wavelet
Transform
Find SD and Energy
values of appropriate
levels
Fix the threshold
GRID CONNECTED MODE - UNDISTURBED SYSTEM
33. Wavelet Transform Based Approach
Extract Neg.
Sequence Voltage
from PCC
Perform Wavelet
Transform
Find SD and Energy
values of appropriate
levels
Detect Disturbance
by Comparing With
Threshold
SYSTEM UNDER DISTURBANCE
34. Frequency Levels Under Consideration
Negative Sequence Voltage taken from PCC
Sample Frequency taken - 1 kHz , Fundamental Frequency - 60 Hz
Wavelet Level Frequency Band (Hz)
Level 1 500 - 1000
Level 2 250 - 500
Level 3 125 - 250
Level 4 62.5 - 125
Level 5 31.25 - 62.5
35. SD3 - Hybrid System Connected to Grid
● Standard Deviation at level n - SDn
Loading
(MW)
Grid
Connected
Islanding L-G Fault L-L Fault
Non -
Linear Load
Switch
0.875 0.00011473 0.03401753 0.00494453 0.00911728 0.01280362
1 0.00011594 0.03204665 0.00494597 0.00911516 0.01280433
1.3 0.00010763 0.02510713 0.00495142 0.00911595 0.01279189
1.75 0.00012745 0.00583276 0.00495896 0.00912749 0.01277151
36. SD4 - Hybrid System Connected to Grid
● Standard Deviation at level n - SDn
Loading
(MW)
Grid
Connected
Islanding L-G Fault L-L Fault
Non -
Linear Load
Switch
0.875 0.00014444 0.03705120 0.01195818 0.02928778 0.02509573
1 0.00015432 0.04406543 0.01196308 0.02921008 0.02509539
1.3 0.00017652 0.02957596 0.01204052 0.02910395 0.02506549
1.75 0.00022712 0.00608325 0.01213379 0.02892913 0.02500862
37. E3 - Hybrid System Connected to Grid
● Energy at level n - En
Loading
(MW)
Grid
Connected
Islanding L-G Fault L-L Fault
Non -
Linear Load
Switch
0.875 0.00130841 0.39045041 0.05639256 0.10401861 0.14671166
1 0.00132418 0.36693973 0.05641079 0.10399256 0.14673016
1.3 0.00123171 0.29207070 0.05647472 0.10399785 0.14659020
1.75 0.00145480 0.06664993 0.05656272 0.10413084 0.14638457
38. E4 - Hybrid System Connected to Grid
● Energy at level n - En
Loading
(MW)
Grid
Connected
Islanding L-G Fault L-L Fault
Non -
Linear Load
Switch
0.875 0.00119563 0.30587180 0.09861007 0.24154712 0.20694654
1 0.00128137 0.36386052 0.09865040 0.24090679 0.20694387
1.3 0.00146062 0.24395503 0.09928896 0.24003145 0.20669716
1.75 0.00187444 0.05016677 0.10005795 0.23858983 0.20622875
39. Inferences
Change in SD and Energy are not abrupt, unlike THD
Thus it's easy to fix a threshold with SD and Energy
SD and Energy can classify the events
Between Islanding and Power Quality Issues
40. Machine Learning
Subfield of Computer Science
Study of pattern recognition
Learns from examples
Training → Learning → Prediction
Category
41. Applications
Voice assistants like Google Voice Search , Cortana and Siri
Product Recommendations on E- Commerce sites
Spam mail classifier
According to Gartner’s Hype Cycle 2015
Most promising technology of future
43. Input Vector ( Feature Vector )
● Loading of DG (MW)
● Standard Deviation at Level 4
● Standard Deviation at Level 3
● Energy at level 4
● Energy at level 3
51. Data Set
No. of examples for each event - 26
No. of events = 5
Total Examples = 26 x 5 = 130
Splitting percentage of data - 70 %
Training Set - 91
52. Inferences
Verified using 3-fold cross validation
ANN can classify and identify the events with excellent accuracy
Prediction accuracy is > 95 %
53. Final Conclusions
WT Indices provide a better prospect on detecting and classifying Islanding
and PQ disturbances in a Hybrid DG System
THD fails to perform accurate classification
Implementation using Machine Learning Classifier
High prediction accuracy
54. Future Scope
Detection of Voltage Swell events are not well performed by WT
Load Switching
Capacitor Bank switching
Performance of WT at noisy environments
Feature Vector Selection
55. Implementation
Implementation as a Web Service
Using Microsoft Azure Machine Learning Suite
API Key -
zogVJpUKUmTEEh3auMuVYZ1q1tBKBWA+tHeZWIyzuG2sYUfxHKJ7F4IpXhGALe5IiVpunz
MHjDF8i0gTImfqfA==
URL -
https://ussouthcentral.services.azureml.net/workspaces/8e3a01b9b5a94cd8be8f69c85b
fd1215/services/923ca965d52a46788d91c32cd9cdc9fd/execute?api-
version=2.0&details=true
59. Reference
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60. Reference
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61. Publications [1] Sleeba Paul Puthenpurakel, Subadhra P.R.,
“Islanding Detection in Grid-Connected 100 KW
Photovoltaic System Using Wavelet Transform”,
International Conference on Emerging Trends in
Smart Grid Technology - INCETS'16, IJIRSET Volume
5, Special Issue 5, April 2016
[2] Sleeba Paul Puthenpurakel, Subadhra P.R.,
“Identification and Classification of Microgrid
Disturbances in a Hybrid Distributed Generation
System Using Wavelet Transform”, International
Conference on Next Generation Intelligent Systems”
