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Sarasij Das
The University of Western Ontario, Canada
1
This presentation is based on the following papers
 Sarasij Das and Tarlochan Sidhu. ‘Application of Compressive Sampling in
Synchrophasor Data Communication in WAMS’, IEEE Transactions on Industrial
Informatics, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553079
 Sarasij Das and Tarlochan Sidhu. 'Reconstruction of Phasor Dynamics at
Higher Sampling Rates using Synchrophasors Reported at Sub-Nyquist Rate.'
Innovative Smart Grid Technologies (ISGT), 2013 IEEE PES, 24-27 Feb 2013,
Washington, D.C
2
 Synchrophasor communication
 Aim
 Basic Theory
 Results
 Conclusion
3
 Dedicated networks preferred due to security
 Utility substations located geographically far away
 Wide area monitoring is time critical application,
communication delay matters
 Fibre optic preferred for low latency
 Fibre optic + long distance + dedicated = High
cost
 At present, 40-300 PMUs installed in a grid
 Number of installed PMUs increasing at higher
rates (1000-10000 in future)
 Higher reporting rate (>60 frames/s) limited by
available bandwidth
 Higher reporting rate and larger no. of PMUs will
lead to huge bandwidth requirement
 Fibre optic networks are costly
 Better network utilization delays requirement of
network upgradation
 Nyquist sampling theorem :
 To avoid aliasing :
Synchrophasor reporting rate twice the
maximum frequency in synchrophasor domain
 System dynamics monitoring not possible with
synchrophasors of Sub-Nyquist reporting rate
6
2s
f f

 To reduce the bandwidth requirement for
synchrophasor communication
 To reconstruct synchrophasors at a higher
rate from a sub-Nyquist reporting rate
7
Use of Compressive Sampling for synchrophasor
communication
8
 Suppose, signal f is sampled at higher than
Nyquist rate
,(y is vector of ‘N’ samples)
Sensing Matrix
 f can be expressed using basis matrix
,(x is coefficient of basis )

9
1 1N N N N
y f  


1 1N N N N
f x  
 
y x A x  
 Suppose, ‘m’ samples (corresponding to sub-
Nyquist rate) are randomly selected from ‘N’
samples.
So,
 If ‘x’ is sparse/near-sparse, ‘x’ can be
recovered from using Compressive
Sampling
10
1 1 1m m N N N N m N N
y x A x      
 
1m
y 
1
0
0
0
0
0
0
N
N Z e r o
x
N Z e r o

 
 
 
 
 
 
 
 
 
 
 
  
Example
11
PMU PMU PMU PMU
Substation PDC Substation PDC
Super-PDC (Control Center PDC)
Substation-PDC
Substation-PDC
 PMUs use low pass filters to remove high
frequencies from estimated synchrophasors
 Std C37.118.1-2011 considers synchrophasor
domain oscillations up to 5 Hz
 1-3 oscillation modes (dominant) usually
appear simultaneously in synchrophasor
domain
12
 Practical Synchrophasors = Near-Sparse
 CS should be designed considering sparsity
of synchrophasors
13
 Block coding : Compresses block of data and
transmits
 Issues with block coding are
- 1 packet loss means loss of multiple phasors
- Additional communication delays
- Need additional processing capability at PMU
 Adaptive coding : Compresses and transmits
data as soon as generated
 Issues are
- Compression ratio low (usually 1.5-2)
- Overall bandwidth savings low due to
communication payloads
- Need additional processing capability at PMU
• Interpolation assumes a signal structure
• Interpolation affected by noise
• Missing data aggravates interpolation
• Consider:
- Bandwidth saving 4
- Phasor reporting rate of PMU 5 frames/s
- Phasor receiving rate@ PDC 20 frames/s
- Interpolation not possible (violation of
Nyquist theorem)
Modulation
frequency
(Hz)
Maximum TVE (%)
Spline Cubic Fourier
Interpolation
CS
5 24 12 7 1
• Synchrophasor reporting rate 10 frames/s
• Synchrophasors reconstructed at PDC 30
frames/s
* IEEE C37.118.1-2011 : specifies maximum 5 Hz
modulation frequency
[1 * co s( )] co s( )]m x a
X X k t k t     
18
Maximum TVE = 0.32%
19
20
21
 CS performs satisfactorily during oscillations,
large step changes, exponentially decay and
steady state
 System dynamics monitoring also be
possible with sub-Nyquist rate
 CS reduces bandwidth requirements
 Please consult the papers for more results
22
Thank You
23

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Applying Compressive Sampling for Efficient Synchrophasor Communication and Reconstruction

  • 1. Sarasij Das The University of Western Ontario, Canada 1
  • 2. This presentation is based on the following papers  Sarasij Das and Tarlochan Sidhu. ‘Application of Compressive Sampling in Synchrophasor Data Communication in WAMS’, IEEE Transactions on Industrial Informatics, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553079  Sarasij Das and Tarlochan Sidhu. 'Reconstruction of Phasor Dynamics at Higher Sampling Rates using Synchrophasors Reported at Sub-Nyquist Rate.' Innovative Smart Grid Technologies (ISGT), 2013 IEEE PES, 24-27 Feb 2013, Washington, D.C 2
  • 3.  Synchrophasor communication  Aim  Basic Theory  Results  Conclusion 3
  • 4.  Dedicated networks preferred due to security  Utility substations located geographically far away  Wide area monitoring is time critical application, communication delay matters  Fibre optic preferred for low latency  Fibre optic + long distance + dedicated = High cost
  • 5.  At present, 40-300 PMUs installed in a grid  Number of installed PMUs increasing at higher rates (1000-10000 in future)  Higher reporting rate (>60 frames/s) limited by available bandwidth  Higher reporting rate and larger no. of PMUs will lead to huge bandwidth requirement  Fibre optic networks are costly  Better network utilization delays requirement of network upgradation
  • 6.  Nyquist sampling theorem :  To avoid aliasing : Synchrophasor reporting rate twice the maximum frequency in synchrophasor domain  System dynamics monitoring not possible with synchrophasors of Sub-Nyquist reporting rate 6 2s f f 
  • 7.  To reduce the bandwidth requirement for synchrophasor communication  To reconstruct synchrophasors at a higher rate from a sub-Nyquist reporting rate 7
  • 8. Use of Compressive Sampling for synchrophasor communication 8
  • 9.  Suppose, signal f is sampled at higher than Nyquist rate ,(y is vector of ‘N’ samples) Sensing Matrix  f can be expressed using basis matrix ,(x is coefficient of basis )  9 1 1N N N N y f     1 1N N N N f x     y x A x  
  • 10.  Suppose, ‘m’ samples (corresponding to sub- Nyquist rate) are randomly selected from ‘N’ samples. So,  If ‘x’ is sparse/near-sparse, ‘x’ can be recovered from using Compressive Sampling 10 1 1 1m m N N N N m N N y x A x         1m y  1 0 0 0 0 0 0 N N Z e r o x N Z e r o                           Example
  • 11. 11 PMU PMU PMU PMU Substation PDC Substation PDC Super-PDC (Control Center PDC) Substation-PDC Substation-PDC
  • 12.  PMUs use low pass filters to remove high frequencies from estimated synchrophasors  Std C37.118.1-2011 considers synchrophasor domain oscillations up to 5 Hz  1-3 oscillation modes (dominant) usually appear simultaneously in synchrophasor domain 12
  • 13.  Practical Synchrophasors = Near-Sparse  CS should be designed considering sparsity of synchrophasors 13
  • 14.  Block coding : Compresses block of data and transmits  Issues with block coding are - 1 packet loss means loss of multiple phasors - Additional communication delays - Need additional processing capability at PMU
  • 15.  Adaptive coding : Compresses and transmits data as soon as generated  Issues are - Compression ratio low (usually 1.5-2) - Overall bandwidth savings low due to communication payloads - Need additional processing capability at PMU
  • 16. • Interpolation assumes a signal structure • Interpolation affected by noise • Missing data aggravates interpolation • Consider: - Bandwidth saving 4 - Phasor reporting rate of PMU 5 frames/s - Phasor receiving rate@ PDC 20 frames/s - Interpolation not possible (violation of Nyquist theorem)
  • 17. Modulation frequency (Hz) Maximum TVE (%) Spline Cubic Fourier Interpolation CS 5 24 12 7 1 • Synchrophasor reporting rate 10 frames/s • Synchrophasors reconstructed at PDC 30 frames/s * IEEE C37.118.1-2011 : specifies maximum 5 Hz modulation frequency [1 * co s( )] co s( )]m x a X X k t k t     
  • 19. 19
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  • 22.  CS performs satisfactorily during oscillations, large step changes, exponentially decay and steady state  System dynamics monitoring also be possible with sub-Nyquist rate  CS reduces bandwidth requirements  Please consult the papers for more results 22