May 2013 - The University of Texas at Austin

Report
POWERLINE COMMUNICATIONS FOR
ENABLING SMART GRID APPLICATIONS
Task ID: 1836.063
Prof. Brian L. Evans
Wireless Networking and Communications Group
Cockrell School of Engineering
The University of Texas at Austin
[email protected]
http://www.ece.utexas.edu/~bevans/projects/plc
May 3, 2013
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
1
Task Description:
Improve powerline communication (PLC) bit rates for monitoring/controlling
applications for residential and commercial energy uses
Anticipated Results:
Adaptive methods and real-time prototypes to increase bit rates in PLC networks
Principal Investigator:
Prof. Brian L. Evans, The University of Texas at Austin
Current Students (with expected graduation dates):
Ms. Jing Lin
Ph.D. (May 2014) Summer 2013 intern at TI
Mr. Yousof Mortazavi
Ph.D. (Dec. 2013)
Mr. Marcel Nassar
Ph.D. (Aug. 2013) Defended PhD April 15, 2013
Mr. Karl Nieman
Ph.D. (May 2015) Summer 2013 intern at Freescale
Industrial Liaisons:
Dr. Anuj Batra (TI), Dr. Anand Dabak (TI), Mr. Leo Dehner (Freescale),
Mr. Michael Dow (Freescale), Dr. Il Han Kim (TI), Mr. Frank Liu (IBM),
Dr. Tarkesh Pande (TI) and Dr. Khurram Waheed (Freescale)
Starting Date: August 2010
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
2
Task Deliverables
Date
Tasks
Dec 2010
Uncoordinated interference in narrowband PLC:
measurements, modeling, and mitigation
May 2011
Testbed #1 based on TI PLC modems to investigate
receiver improvements
Dec 2011
Narrowband PLC channel/noise: measurements/modeling
May 2012
Standard-compliant receiver methods (3x bit rate increase)
Dec 2012
Testbed #2 based on Freescale PLC modems to investigate
transmitter improvements (2x bit rate increase)
On-going
Testbed #3 based on NI equipment to map noise mitigation
algorithms onto FPGAs
Testbed #4 for two-transmitter two-receiver (2x2) systems
based on TI PLC modems to investigate scalability
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
3
Recent Project Highlights
• Paper in Smart Grid Special Issue (Sep. 2012)
• IEEE Signal Processing Magazine (impact factor 4.066)
• Paper on channel impairments, noise, and standards
• Co-authored with Dr. Anand Dabak (TI) and Dr. Il Han Kim (TI)
• Channel Model Adopted (Oct. 2012)
• Reference model for IEEE 1901.2 Standard for Low Frequency
Narrow Band Power Line Communications for Smart Grid App.
• Mr. Marcel Nassar, Dr. Anand Dabak (TI), Dr. Il Han Kim (TI), et al.
• SRC Technical Transfer Talk (Dec. 2012)
• Best Paper Award (Mar. 2013)
• 2013 IEEE Int. Symp. On Power Line Comm. and Its Applications
• Co-authored with Dr. Khurram Waheed (Freescale)
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
Smart Grid
4
Wind farm
HV-MV Transformer
Central power plant
Grid status monitoring
Utility control center
Smart meters
Integrating distributed
energy resources
Houses
Offices
Device-specific billing
Automated control for
smart appliances
Medium Voltage (MV)
1 kV – 33 kV
Industrial plant
High Voltage (HV)
33 kV – 765 kV
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
5
ISTOCKPHOTO.COM/© SIGAL SUHLER MORAN
Smart Grid Goals
• Accommodate all generation types
Renewable energy sources
Energy storage options
• Improve operating efficiencies
Scale voltage with energy demand
Reduce peak demand
Analyze customer load profiles and
system load snapshots
• Improve system reliability
Power quality monitoring
Remote disconnect/reconnect
Outage/restoration event notification
Enabled by
smart meter
communications
• Inform customer
Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
6
Smart Meter Communications
Communication backhaul
carries traffic between
concentrator and utility
on wired or wireless links
Local utility
Data
concentrator
Low voltage (LV)
under 1 kV
MV-LV transformer
Smart meter communications
between smart meters and
data concentrator via
powerline or wireless links
Smart meters
Home area data networks
connect appliances, EV charger and smart
meter via powerline or wireless links
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
7
Powerline Communications (PLC)
Categories
Band
Bit Rates
Coverage
Narrowband
3-500
kHz
~500 kbps
• (ITU) PRIME, G3
MultiSmart meter
• ITU-T G.hnem
kilometer communication
• IEEE P1901.2
Broadband
1.8-250
MHz
~200
Mbps
<1500 m
Enables
• HomePlug
Home area
• ITU-T G.hn
data networks
• IEEE P1901
• Use orthogonal frequency division multiplexing (OFDM)
• Communication challenges
o Channel distortion
o Non-Gaussian noise
Standards
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
8
OFDM Systems in Impulsive Noise
• FFT in receiver spreads impulsive energy over all tones
Signal-to-noise ratio (SNR) in each subchannel decreases
• Narrowband PLC systems operate -5 dB to 5 dB in SNR
Data subchannels carry same number of bits (1-4) in current standards
Each 3 dB increase in SNR on data subchannels could give extra bit
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
9
Narrowband PLC Systems
• Problem: Non-Gaussian impulsive noise is #1 limitation to
communication performance yet traditional communication
system design assumes additive noise is Gaussian
• Goal: Improve comm. performance in impulsive noise
• Approach: Statistical modeling of impulsive noise
• Solution #1: Receiver design (standard compliant)
Parametric Methods
Nonparametric Methods
Listen to environment
No training necessary
Find model parameters
Learn statistical model from
communication signal structure
Use model to mitigate noise
Exploit sparsity to mitigate noise
• Solution #2: Joint transmitter-receiver design
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
10
Narrowband PLC Impulsive Noise
Cyclostationary Noise
Asynchronous Noise
Example: rectified power supplies
Example: uncoordinated interference
Rx Receiver
Dominant in outdoor PLC
Increases with widespread deployment
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
11
Non-Parametric Mitigation Methods
• Exploit sparsity of impulsive noise in time domain
Build statistical model each OFDM symbol
using sparse Bayesian learning (SBL)
At receiver, null tones contain only Gaussian + impulsive noise
time
• SNR gain vs. conventional OFDM systems at symbol error rate 10-4
System
Uncoded
Coded
Noise
SBL w/
null tones
SBL w/
all tones
SBL w/ decision
feedback
GMM
8 dB
10 dB
-
MCA
6 dB
7 dB
-
GMM
2 dB
7 dB
9 dB
MCA
1.75 dB
6.75 dB
8.75 dB
Complex, 128-point FFT, QPSK, data tones 33-104, rate ½ conv. code
Asynchronous Gaussian mixture model and Middleton Class A noise
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
12
Time Domain Interleaving
Bursts span consecutive OFDM symbols
Coded performance in cyclostationary noise
Interleave
Bursts spread over many OFDM symbols
Complex OFDM, 128-point FFT, QPSK,
data tones 33-104, rate ½ conv. code
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
13
Time-Domain Interleaving
Coded performance in cyclostationary noise
Burst duty cycle 10%
Time-domain interleaving over an AC cycle
Current PLC standards use frequency-domain interleaving (FDI)
Burst duty cycle 30%
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
14
Testbed #1
• Adaptive signal processing algorithms for bit loading and
interference mitigation
Hardware
Software
• NI x86 controllers stream data
• Transceiver algorithms in C on x86
• NI cards generates/receives analog signals • Desktop LabVIEW configures system
• TI front end couples to power line
and visualizes results
1x1 Testbed
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
15
Testbed #2: Noise Playback/Analysis
• G3 link using two Freescale PLC modems
• Freescale software tools allow frame-by-frame analysis
• Test setup allows synchronous noise injection into power line
Freescale PLC G3-OFDM Modem
• One modem to sample
Freescale PLC Testbed
powerline noise in field
• Collected 16k 16-bit 400
kS/s at each location
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
16
Testbed #2: Cyclic Power Line Noise
• Analyzed cyclic properties of PLC noise measurements
10
subcarrier
50
0
-10
40
-20
-30
30
-40
10
20
30
40
OFDM Symbol
50
subcarrier
50
noise power vs avg (dB)
• Developed cyclic bit loading method for transmitter
D8PSK
DQPSK
DBPSK
40
1. Receiver measures noise
power over half AC cycle
2. Feedback modulation
map to transmitter
3. Allocate more bits in
higher SNR subchannels
ROBO
30
NONE
10
20
30
40
OFDM Symbol
50
2x increase in bit rate
Won Best Paper Award at ISPLC
17
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
Testbed #3: FPGA Implementation
• Built NI/LabVIEW testbed with real-time link (G3 settings)
• Redesigned parametric impulsive noise mitigation algorithm
• Converted matrix operations to distributed calculations on scalars
• Based on approximate message passing (AMP) framework
• Mapped transceiver to fixed-point data/math using Matlab
• Synthesis: LabVIEW DSP Diagram to Xilinx Vertex 5 FPGAs
Received QPSK constellation at equalizer output Utilization
FPGA
conventional receiver
with AMP
Trans.
Rec.
AMP+Eq
1
2
3
total slices
32.6% 64.0%
94.2%
slice reg.
15.8% 39.3%
59.0%
slice LUTs
17.6% 42.4%
71.4%
DSP48s
2.0%
7.3%
27.3%
blockRAMs
7.8% 18.4%
29.1%
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
18
Testbed #4: 2x2 PLC (On-Going)
• Goal: Improve communication performance by another 2x
• One phase, neutral, ground for 2x2 differential signaling
• Crosstalk between two channels due to energy coupling
Frequency response of a direct channel
Crosstalk highly correlated with direct channel response
Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion
19
Conclusion
• PLC systems are interference limited
• Statistical models for interference
• Cyclostationary model for impulsive noise synchronous to AC cycle
• Gaussian mixture model for asynchronous impulsive noise
• Interference management
• Cyclic bit loading to double bit rates in cyclostationary noise
• Time-domain interleaving to mitigate cyclostationary noise followed
by receiver impulsive noise mitigation
• Mapping impulsive noise mitigation algorithms to FPGAs
• Poor: Non-parametric sparse Bayesian learning algorithms
• Good: Parametric distributed approximate message algorithms
http://users.ece.utexas.edu/~bevans/projects/plc/index.html
20
Our Publications
Tutorial/Survey Article
• 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. Impact Factor 4.066.
Journal Paper
• J. Lin, M. Nassar and B. L. Evans, “Impulsive Noise Mitigation in Powerline
Communications using Sparse Bayesian Learning”, IEEE Journal on Selected Areas in
Communications, Special Issue on Smart Grid Communications, Jul. 2013. Impact
Factor 3.413.
Conference Publications (more on next slide)
• J. Lin and B. L. Evans, “Non-parametric Mitigation of Periodic Impulsive Noise in
Narrowband Powerline Communications”, Proc. IEEE Global Communications
Conference, Dec. 2013, Atlanta, GA USA, submitted.
21
Our Publications
Conference Publications (more on next slide)
• M. Nassar, P. Schniter and B. L. Evans, “Message-Passing OFDM Receivers for
Impulsive Noise Channels”, Proc. Asilomar Conf. on Signals, Systems, and Computers,
Nov. 2013, Pacific Grove, CA, submitted.
• K. F. Nieman, M. Nassar, J. Lin and B. L. Evans, “FPGA Implementation of a MessagePassing OFDM Receiver for Impulsive Noise Channels”, Proc. Asilomar Conf. on
Signals, Systems, and Computers, Nov. 2013, Pacific Grove, CA, submitted.
• K. Nieman, J. Lin, M. Nassar, K. Waheed, and B. L. Evans, “Cyclic Spectral Analysis of
Power Line Noise in the 3-200 kHz Band”, Proc. IEEE Int. Sym. on Power Line Comm.
and Its App., Mar. 2012, Johannesburg, South Africa. Best Paper Award.
• J. Lin and B. L. Evans, “Cyclostationary Noise Mitigation in Narrowband Powerline
Communications”, Proc. APSIPA Annual Summit and Conf., invited paper, Dec. 2012,
Hollywood, CA USA.
• 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. 2012, Kyoto, Japan
22
Our Publications
Conference Publications (more on next slide)
• 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. 2011, Houston, TX USA.
• 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. 2011, Houston, TX USA.
Standards Contribution
• A. Dabak, B. Varadrajan, I. H. Kim, M. Nassar, and G. Gregg, “Appendix for noise
channel modeling for IEEE P1901.2”, IEEE P1901.2 Std., June 2011, doc: 2wg-110134-05-PHM5. Adopted as reference noise model in Oct. 2012 ballot.
23
Thank you for your attention…
Questions?
24
Backup Slides
25
Today’s Power Grids in USA
• 7 large-scale power grids each managed by a regional utility company
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%
Energy demand correlated with time of day
Effect of plug-in electric vehicles (EVs) on energy demand uncertain
Generation cost 30x higher during peak times vs. normal load
• 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
26
Comparison of Wireless & PLC Systems
Wireless
Communications
Narrowband PLC (3-500 kHz)
Time selectivity
Time-selective fading and
Doppler shift (cellular)
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
Propagation
Dynamically changing
Determinism from fixed grid topology
Synchronization
Varies
AC main power frequency
Additive noise/
interference
Assumed stationary
and Gaussian
Gaussian plus non-Gaussian noise
dominated by cyclostationary component
Asynchronous
interference
Uncoordinated users in
Wi-Fi bands;
Frequency reuse in
cellular
Due to power electronics and
uncoordinated users using other
standards
Standardized for
Wi-Fi and cellular
Number of wires minus 1;
G.9964 standard for broadband PLC
MIMO
27
PLC Noise Scenarios
Background Noise
Asynchronous
Impulsive Noise
Cyclostationary Noise
-50
-100
-150
•
•
•
•
time
0
100
200
300
Frequency (kHz)
400
500
Spectrally shaped noise
Decreases with frequency
Superposition of lowerintensity sources
Includes narrowband
interference
•
•
•
•
Cylostationary in time and
frequency
Synchronous and
asynchronous to AC main
frequency
Comes from rectified and
switched power supplies
(synchronous), and electrical
motors (asynchronous)
Dominant in narrowband PLC
•
•
•
•
•
Impulse duration from
micro to millisecond
Random inter-arrival time
50dB above background
noise
Caused by switching
transients and
uncoordinated interference
Present in narrowband
and broadband PLC
28
Cyclostationary Noise
Noise Sources
Noise Trace
29
Uncoordinated Interference Results
Homogeneous PLC Network
General PLC Network
30
Cyclostationary Noise Modeling
Measurement data
from UT/TI field trial
Cyclostationary
Gaussian Model
Proposed model uses
three filters [Nassar12]
[Katayama06]
Demux
Period is one half
of an AC cycle
s[k] is zero-mean
Gaussian noise
Adopted by IEEE P1901.2
narrowband PLC standard
31
Asynchronous Noise Modeling
Dominant Interference Source
Impulse rate l
Impulse duration m
Ex. Rural areas,
industrial areas w/
heavy machinery
Middleton Class A
Ex. Semi-urban
areas, apartment
complexes
Middleton Class A
Ex. Dense urban
and commercial
settings
Gaussian Mixture
Distribution [Nassar11]
Homogeneous PLC Network
li = l, mi = m, g(di) = g0
Distribution [Nassar11]
General PLC Network
li, mi, g(di) = gi
Model [Nassar11]
Middleton Class A is a special case of the Gaussian Mixture Model.
32
Parametric vs. Nonparametric Methods
Parametric
Nonparametric
Must build a statistical
model of the noise
Yes
No
Requires training data to
compute model parameters
Yes
No
Degrades in performance
due to model mismatch
Yes
No
Has high complexity when
receiving message data
No
Yes
33
Asynchronous Noise
• Sparse in time domain
-1
10
~10dB
time
~6dB
• Learn statistical model
• Use sparse Bayesian
learning (SBL)
• Exploit sparsity in time
domain [Lin11]
• SNR gain of 6-10 dB
• Increases 2-3 bits per tone
for same error rate - OR • Decreases bit error rate by
10-100x for same SNR
Symbol Error Rate
-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.
34
Performance w/o Error Correction
Gaussian mixture
model noise
Non-parametric
methods in blue
Parametric
methods in red
Proposed
NSI
CS+LS: [Caire08]
MMSE: [Haring02]
SBL: [Lin11]
35
Performance w/ Error Correction
Proposed
Non-parametric
methods in blue
Parametric
methods in red
NSI
NSI
Gaussian mixture model noise
36
Power Line Noise at Residential Site
frequency sweep
f = 170 kHz
narrowband
f = 140 kHz
complex spectrum
f = 30-120 kHz
37
Analysis of Residential Noise
though spectrally complex,
many components have
strong stationarity at 120 Hz
38
Testbed #1
• 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
• Investigate
• Adaptive signal processing algorithms
• Improved communication performance 2-3x
39
Message-Passing OFDM Receiver
16-bit DAC
10 MSps
sample
rate
conversion
400 kSps
256 IFFT
w/ 22 CP
insertion
368.3 kSps
zero
padding
(null tones)
103.6 kSps
generate
complex
conjugate pair
51.8 kSps
differential MCX pair
8.6 kSps
reference
symbol LUT
LabVIEW RT
LabVIEW DSP Design Module
NI 5781
14-bit ADC
FlexRIO FPGA Module 1 (G3TX)
10 MSps
sample
rate
conversion
400 kSps
time and
frequency
offset
correction
400 kSps
256 FFT
w/ 22 CP
removal
368.3 kSps
null tone
and active
tone
separation
NI 5781
Subtract
noise
estimate from
active tones
AMP noise
estimate
368.3 kSps
256 FFT, tone
select
51.8 kSps
Host Computer
data and
reference
symbol deinterleave
8.6 kSps
ZF channel
estimation/
equalization
43.1 kSps
BER/SNR
calculation w/
and w/o AMP
43.1 kSps
51.8 kSps
LabVIEW DSP Design Module
LabVIEW DSP Design Module
FlexRIO FPGA Module 2 (G3RX)
FlexRIO FPGA Module 3 (AMPEQ)
Example Input Noise
LabVIEW
RT controller
51.8 kSps
184.2 kSps
testbench control/data visualization
data symbol
generation
43.2 kSps
data and
reference
symbol
interleave
Resource Utilization
LabVIEW RT
RT controller

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