Seminar at the American University of Beirut
Co-sponsored by the local IEEE chapter
Powerline Communications for
Smart Grids
Prof. Brian L. Evans
Department of Electrical & Computer Engineering
Wireless Networking & Communications Group
The University of Texas at Austin
17 July 2012
In collaboration with PhD students Ms. Jing Lin, Mr. Marcel Nassar and
Mr. Yousof Mortazavi and TI R&D engineers Dr. Anand Dabak and Dr. Il Han Kim
Outline
• Research group overview
• Smart power grids
• Powerline noise
Cyclostationary
Gaussian mixture
• Testbed
• Conclusion
My visits to Lebanon
1
Research Group
• Present: 9 PhD, 1 MS, 5 BS
• Alumni: 20 PhD, 9 MS, 140 BS
• Communication systems
Powerline communication systems (design tradeoffs)
Cellular, Wimax & Wi-Fi (interference modeling & mitigation)
Mixed-signal IC design (mostly digital ADCs and synthesizers)
Underwater acoustic communications (large receiver arrays)
• Video processing (rolling shutter artifact reduction)
• Electronic design automation (EDA) tools/methods
• Part of Wireless Networking & Communications Group
160 graduate students,18 faculty members, 12 affiliate companies
wncg.org
2
Research Group – Completed Projects
20 PhD and 9 MS alumni
System
SW release
Prototype
Companies
Matlab
DSP/C
Freescale, TI
MIMO testbed
LabVIEW
LabVIEW/PXI
Oil&Gas
Wimax/LTE
resource allocation
LabVIEW
DSP/C
Freescale, TI
Camera
image acquisition
Matlab
DSP/C
Intel, Ricoh
Display
image halftoning
Matlab
C
HP, Xerox
video halftoning
Matlab
fixed point conv.
Matlab
FPGA
Intel, NI
distributed comp.
Linux/C++
Navy sonar
Navy, NI
ADSL
EDA tools
Contribution
equalization
DSP Digital Signal Processor
MIMO Multi-Input Multi-Output
LTE
PXI
Qualcomm
Long-Term Evolution (cellular)
PCI Extensions for Instrumentation
3
Research Group – Current Projects
6 PhD and 4 MS students
System
Powerline
comm.
Contributions
noise reduction;
MIMO testbed
SW release
Prototype
Companies
LabVIEW
LabVIEW /
PXI chassis
Freescale,
IBM, TI
Matlab
FPGA
Intel, NI
Wimax, LTE interference
& WiFi
reduction
time-based ADC
IBM 45nm
Underwater space-time methods;
comm.
MIMO testbed
Matlab
Lake Travis
testbed
Navy
Cell phone
camera
reducing rolling
shutter artifacts
Matlab
Android
TI
EDA Tools
reliability patterns
MIMO Multi-Input Multi-Output
NI
PXI
PCI Extensions for Instrumentation
4
Smart Grid Goals
• Accommodate all generation types
Renewable energy sources
Energy storage options
• Enable new products, service and markets
• Improve asset utilization and operating efficiencies
Scale voltage with energy demand
Generation cost 30x higher during peak times vs. normal load (USA)
Plug-in vehicles create unpredictability in residential power load
• Improve system reliability including power quality
• Enable informed customer participation
Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA
5
A Smart Grid
Communication to
isolated area
Power generation
optimization
Integrating
alternative energy
sources
Load balancing
Disturbance
monitoring
Smart metering
Electric car charging &
smart billing
Source: ETSI
6
Power Lines
• Built for unidirectional energy flow
• Bidirectional information flow
throughout smart grid will occur
Low Voltage (LV)
under 1 kV
High Voltage (HV)
33 kV – 765 kV
Medium Voltage (MV)
1 kV – 33 kV
Transformer
Source: ERDF
7
Today’s Situation in USA
• 7 large-scale power grids each managed by a regional utility company
Western US, Eastern US, Texas, and others
700 GW generation capacity in total for long-haul high-voltage power transmission
Synchronized independently, and exchange power via DC transfer
• 130+ medium-scale power grids each managed by a local utility
Local power distribution to residential, commercial and industrial customers
• Heavy penalties in US for blackouts (2003 legislation)
Utilities generate expected energy demand plus 12%
• Traditional ways to increase capacity to meet peak demand increase
Build new large-scale power generation plant at cost of $1-10B if permit issued
Build new transmission line at $0.6M/km which will take 5-10 years to complete
Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA
8
Smart Power Meters at Customer Site
• Enable local utilities to improve
Operating efficiency
System reliability
Customer participation
• Automatic metering infrastructure functions
Interval reads (every 1/15/30/60 minutes) and on-demand reads and pings
Transmit customer load profiles and system load snapshots
Power quality monitoring
Remote disconnect/reconnect and outage/restoration event notification
• Need low-delay highly-reliable communication link to local utility
• 75M smart meters sold in 2011 (20% increase vs. 2010)
Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA
9
Local Utility Powerline Communications (PLC)
• PLC modems (PRIME, etc.) use carrier sensed multiple access
to determine when the medium is available for transmission
• MV router plays similar role as a Wi-Fi access point
10
PLC In Different Frequency Bands
Category
Band
Ultra
Narrowband
Narrowband
Broadband
Bit Rate
Applications
Standards
0.3 – 3
kHz
~100 bps
• Automatic meter reading
• Outage detection
• Load control
N/A
3 – 500
kHz
• Smart metering
~500 kbps • Real-time energy
management
1.8 – 250
~200 Mbps • Home area networks
MHz
• PRIME, G3
• ITU-T G.hnem
• IEEE P1901.2
• HomePlug
• ITU-T G.hn
• IEEE P1901
All of the above standards are based on multicarrier communications using orthogonal
frequency division multiplexing (OFDM).
11
Comparison Between Wireless and PLC Systems
Wireless Communications
Narrowband PLC (3-500 kHz)
Time selectivity
Due to node mobility
From random load variations
due to switching activity
Time-varying
stochastic model
Doppler spectrum
Periodic with period of half AC main freq.
plus lognormal time-selective fading
Power loss vs.
distance d
d –n/2 where n is
propagation constant
e – a(f) d plus additional attenuation when
passing through transformers
Additive noise
Assumed stationary
and Gaussian
non-Gaussian and impulsive with
dominant cyclostationary component
Propagation
Dynamically changing
Determinism from fixed grid topology
Interference
limited
In Wi-Fi deployments and
increasing in cellular
Increasing due to uncoordinated users
using different standards
Standardized for
Wi-Fi and cellular
Order of #wires minus 1;
G.9964 standard for broadband PLC
Difficult across network
AC main frequency makes simpler
MIMO
Synchronization
12
Physical Layer Parameters for
OFDM Narrowband PLC Standards
CENELEC A band is from 3 to 95 kHz. FCC band is from 34.375 to 487.5 kHz.
PRIME and G3 use real-valued baseband OFDM. Others are complex-valued.
13
Sources of Powerline Noise
Uncoordinated
transmission
Power line
disturbance
Electronic devices
Taken from
a local utility
point of view
14
Types of Powerline Noise
Background Noise
Cyclostationary Noise
Impulsive Noise
-50
-100
-150
0
100
200
300
Frequency (kHz)
400
time
500
Spectrally shaped noise
with 1/f spectral decay
Period synchronous
to half of the AC cycle
Random impulsive bursts
Superposition of low
intensity noise sources
Switching power supplies
and rectifiers
Circuit transient noise and
uncoordinated interference
Present in all PLC
Dominant in
Narrowband PLC
Dominant in
Broadband PLC
15
Non-Gaussian Noise: Challenge to PLC
• Performance of conventional communication system degrades in
non-AWGN environment
• Statistical modeling of powerline noise
• Noise mitigation exploiting the noise model or structure
Listen to the environment
Estimate noise model
Use model or structure to mitigate noise
16
Cyclostationary Noise Modeling
in Narrowband PLC (3-500 kHz)
1. M. Nassar, A. Dabak, I. H. Kim, T. Pande and B. L. Evans, “Cyclostationary
Noise Modeling In Narrowband Powerline Communication For Smart Grid
Applications”, Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Proc.,
Mar. 25-30, 2012, Kyoto, Japan.
2. M. Nassar, J. Lin, Y. Mortazavi, A. Dabak, I. H. Kim and B. L. Evans, “Local
Utility Powerline Communications in the 3-500 kHz Band: Channel
Impairments, Noise, and Standards”, IEEE Signal Processing Magazine,
Special Issue on Signal Processing Techniques for the Smart Grid, Sep.
2012, 14 pages.
17
Cyclostationary Noise: Field Measurement
Medium Voltage Site
Low Voltage Site
Data collected jointly with Aclara and
Texas Instruments near St. Louis, MO USA
18
Noise Modeling
• Linear periodically time-varying (LPTV) system model
H1
vR
H2
N
n  RN
…
HM
Hi - Linear time invariant filter
N - Period in samples
o A period is partitioned into M segments
o Noise within each segment is stationary, i.e. modeled by an LTI system
Segment: 1
23
19
Model Fitting
• LPTV model (M = 3) captures temporal-spectral cyclostationarity
Measurement data
Noise synthesized from model
The proposed TI-Aclara-UT model was adopted in the IEEE P1901.2 narrowband PLC standard
20
A Lebanese Interlude
Ghine
Jbeil/Byblos
Not
shown:
Baalbek,
Beirut,
Tripoli,
Tyre,
Zahle,
and other
great places
Baruk
Cedars
Beiteddine
Sidon
Jezzine
21
Impulsive Noise in Broadband PLC:
Modeling and Mitigation
3. M. Nassar, K. Gulati, Y. Mortazavi, and B. L. Evans, “Statistical Modeling of
Asynchronous Impulsive Noise in Powerline Communication Networks”, Proc.
IEEE Int. Global Communications Conf., Dec. 5-9, 2011, Houston, TX USA.
4. J. Lin, M. Nassar and B. L. Evans, “Non-Parametric Impulsive Noise
Mitigation in OFDM Systems Using Sparse Bayesian Learning”, Proc. IEEE
Int. Global Communications Conf., Dec. 5-9, 2011, Houston, TX USA.
22
Sources of Impulsive Noise
Wireless
Emissions
In-home PLC
Switching Transients
Total interference at receiver:
Uncoordinated
Meters
(coexistence)
Interference
from source i
23
Statistical-Physical Modeling
• Interference from a single source
Noise envelope
k pulses in a window of duration T
(k)
(j)
Tk
Pulse emission duration
(1)
(2)
τj
Pulse arrival time
t=0
Emission duration: geometrically distributed with mean μ
Pulse arrivals: homogeneous Poisson point process with rate λ
Assuming channel between interference source and receiver has flat fading
24
Statistical-Physical Modeling (cont.)
• Aggregate interference from multiple sources
Dominant interference source
Impulse rate 
Impulse duration 
Ex. Rural areas,
industrial areas with
heavy machinery
Homogeneous network
i  , i  , (di)  
Ex. Semi-urban
areas, apartment
complexes
General (heterogeneous) network
i, i, (di)  i
Ex. Dense urban
and commercial
settings
Middleton class A
A  
A (d ) E[h 2 B 2 ]

2
Middleton class A
A  M 
A E[h 2 B 2 ]

2M
Gaussian mixture
model
π and σ2 in [3]
25
Model Fitting: Tail Probability
Homogeneous PLC Network
General PLC Network
Middleton Class A model is a special case of the Gaussian mixture model (GMM)
26
OFDM Systems in Impulsive Noise
• FFT spreads out impulsive energy across all tones
SNR in each tone is decreased
Receiver performance degrades
27
Impulsive Noise Mitigation in OFDM Systems
• A linear system with Gaussian disturbance
v
y  Fe  FHF * x  Fn  Fe  v,
g

v ~ CN (x,  2 I )
Estimate the impulsive noise and remove it from the received signal
yˆ  y  Feˆ  x  g
Apply standard OFDM decoder as if only AWGN were present
28
Parametric Vs. Non-Parametric Methods
• Noise in different PLC networks has different statistical models
• Mitigation algorithms need to be robust in different noise scenarios
Parametric Methods
Non-Parametric Methods
Assume parameterized
noise statistics
Yes
No
Performance degradation
due to model mismatch
Yes
No
Training needed
Yes
No
29
Non-Parametric Mitigation Using Null Tones
J : Index set of null tones
FJ : DFT sub-matrix
e: Impulsive noise in time domain
g: AWGN with unknown variance
• A compressed sensing problem
Exploiting the sparse structure of the time-domain impulsive noise
• Sparse Bayesian learning (SBL)
Proposed initially by M. L. Tipping
A Bayesian inference framework with sparsity promoting prior
30
Sparse Bayesian Learning
• Bayesian inference
Sparsity promoting prior:
Likelihood:
Posterior probability:
e |  ~ CN (0, ),   diag ( )
yJ |  , 2 ~ CN (0, FF *   2 I )
e | yJ ;  , 2 ~ CN (, e )
• Iterative algorithm
Step 1: Maximum likelihood estimation of hyper-parameters (γ, σ2)
Solved by expectation maximization (EM) algorithm (e is latent variable)
Step 2: Estimate e from the mean of the posterior probability, go to Step 1
31
Non-Parametric Mitigation Using All Tones
• Joint estimation of data and noise
J : Index set of data tones
z : Received signal in frequency domain
Treat the received signal in data tones as additional hyper-parameters
Estimate of zJ is sent to standard OFDM equalizer and symbol detector
32
Simulated Communication Performance
• Interference in time domain
-1
10
~10dB
time
Use sparse Bayesian learning
Exploit sparsity in time domain
• SNR gain of 6-10 dB
Increases 2-3 bits per tone for
same error rate - OR Decreases bit error rate by 10100x for same SNR
Symbol Error Rate
• Learn statistical model
~6dB
-2
10
-3
10
-4
10
No cancellation
SBL w/ null tones
-5
SBL w/ all tones
10
-10
-5
0
SNR (dB)
5
10
Transmission places 0-3 bits at each tone
(frequency). At receiver, null tone carries 0 bits
and only contains impulsive noise.
33
Our PLC Testbed
• Quantify application performance vs. complexity tradeoffs
Extend our real-time DSL testbed (deployed in field)
Integrate ideas from multiple narrowband PLC standards
Provide suite of user-configurable algorithms and system settings
Display statistics of communication performance
• 1x1 PLC testbed (completed)
Adaptive signal processing algorithms
Improved communication performance 2-3x on indoor power lines
• 2x2 PLC testbed (on-going)
Use one phase, neutral and ground
Goal: Improve communication performance by another 2x
34
Our PLC Testbed
Hardware
Software
• National Instruments (NI) controllers stream • NI LabVIEW Real-Time system runs
data
transceiver algorithms
• NI cards generates/receives analog signals
• Desktop PC running LabVIEW is used
• Texas Instruments (TI) analog front end
as an input and visualization tool to
couples to power line
display important system parameters.
1x1 Testbed
35
Conclusion
• Communication performance of PLC systems
Primarily limited by non-Gaussian noise
• Proposed statistical models for
Cyclostationary noise in narrowband PLC systems
Impulsive noise in broadband PLC systems (also useful in narrowband PLC)
• Proposed non-parametric impulsive noise mitigation algorithms
OFDM PLC systems (G3, IEEE P1901.2, ITU G.hnem, etc.)
Robust in noise scenarios tested
6-10 dB SNR gain over conventional OFDM receivers
36
Thank you …
37
Simulated Performance
• Symbol error rate in different noise scenarios
~10dB
~6dB
~6dB
~8dB
~4dB
Gaussian mixture model
Middleton class A model
• MMSE w/ (w/o) CSI: Parametric estimator assuming known (unknown) statistical parameters of noise
• CS+LS: A compressed sensing and least squares based algorithm
38