Powerline Communications for Smart Grid

Transcript Powerline Communications for Smart Grid

Semiconductor Research Corporation Presentation Texas Analog Center of Excellence, The University of Texas at Dallas

Smart Grid Communications

Prof. Brian L. Evans

Dept. of Electrical & Computer Engineering Wireless Networking & Communications Group The University of Texas at Austin 14 December 2012

In collaboration with UT Austin PhD students Ms. Jing Lin, Mr. Yousof Mortazavi, Mr. Marcel Nassar and Mr. Karl Nieman; Freescale engineers Mr. Mike Dow and Dr. Khurram Waheed; and TI engineers Dr. Anand Dabak and Dr. Il Han Kim

http://users.ece.utexas.edu/~bevans/projects/plc/index.html

Outline

• • • • •

Research group Smart power grids Powerline noise Cyclostationary Gaussian mixture Testbeds Conclusion

IEEE Signal Processing Magazine

Special Issue on Signal Processing Techniques for the Smart Grid, September 2012. 1

Embedded Signal Processing Laboratory

• • • • • • Present: 9 PhD, 0 MS, 5 BS Alumni: 20 PhD, 9 MS, 140 BS Communication systems Hugo Powerline communication systems (

design tradeoffs

) Wi-Fi (

interference modeling & mitigation for ISM bands

) Cloud Radio Access Networks (

LTE basestation coordination

) Mixed-signal IC design (

mostly digital ADCs and synthesizers

) Video processing (

rolling shutter artifact reduction

) Electronic design automation (EDA) tools/methods Part of Wireless Networking & Communications Group 160 grad students, 20 faculty members, 13 affiliate companies Marcus Chao Yousof Karl Jing Debarati Marcel Kyle 2

Research Group – Completed Projects

20 PhD and 9 MS alumni

System

ADSL Wimax/LTE Underwater comm.

Camera Display EDA tools

Contribution

equalization MIMO testbed resource allocation space-time proc.; MIMO testbed image acquisition image halftoning video halftoning fixed point conv.

distributed comp.

SW release

Matlab LabVIEW LabVIEW Matlab Matlab Matlab Matlab Matlab Linux/C++

Prototype

DSP/C LabVIEW/PXI DSP/C Lake Travis testbed DSP/C C FPGA Navy sonar

Companies

Freescale, TI Oil&Gas Freescale, TI Navy Intel, Ricoh HP, Xerox Qualcomm Intel, NI Navy, NI DSP Digital Signal Processor LTE Long-Term Evolution (cellular) MIMO Multi-Input Multi-Output PXI PCI Extensions for Instrumentation 3

Research Group – Current Projects

System

Powerline comm.

Wi-Fi Cellular Handheld camera EDA tools

Contributions

noise reduction; MIMO testbed interference reduction time-based ADC cloud radio access network architecture reducing rolling shutter artifacts reliability patterns 9 PhD students

SW release

LabVIEW Matlab

Prototype

LabVIEW / PXI chassis FPGA

Companies

Freescale, IBM, TI Intel, NI Matlab IBM 45nm Android Huawei TI NI MIMO Multi-Input Multi-Output PXI PCI Extensions for Instrumentation 4

Outline

• • • • •

Research group Smart power grids Powerline noise Cyclostationary Gaussian mixture Testbeds Conclusion

IEEE Signal Processing Magazine

Special Issue on Signal Processing Techniques for the Smart Grid, September 2012. 5

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 6

Smart Grid Goals

• • • • Accommodate all generation types Renewable energy sources Energy storage options Improve asset utilization and 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 Enable informed customer participation

Enabled by smart meter communications

Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA 7

Smart Grid

Central power plant Wind farm HV-MV Transformer Grid status monitoring Utility control center Smart meters Integrating distributed energy resources Houses Device-specific billing Offices Automated control for smart appliances Medium Voltage (MV) 1 kV – 33 kV Industrial plant High Voltage (HV) 33 kV – 765 kV 8

Smart Grid Communications

Local utility Communication backhaul carries traffic between concentrator and utility on wired or wireless links

Data concentrator Smart meters

Low voltage (LV) under 1 kV MV-LV transformer Smart meter communications between smart meters and data concentrator via powerline or wireless links Home area data networks connect appliances, EV charger and smart meter via powerline or wireless links 9

Powerline Communications (PLC)

Categories Narrowband Band 3-500 kHz Bit Rates ~500 kbps Coverage Multi kilometer Enables Smart meter communication • • • Standards (ITU) PRIME, G3 ITU-T G.hnem

IEEE P1901.2

Broadband 1.8-250 MHz ~200 Mbps <1500 m Home area data networks • • • HomePlug ITU-T G.hn

IEEE P1901 • • Use orthogonal frequency division multiplexing (OFDM) Communication challenges o Channel distortion o Non-Gaussian noise 10

Comparison Between Wireless and PLC Systems

Time selectivity Power loss vs. distance d Propagation Synchronization

Additive noise/ interference Asynchronous interference MIMO

Wireless Communications

Time-selective fading and Doppler shift (cellular)

– n /2 where n is propagation constant Dynamically changing Varies

Assumed stationary and Gaussian Uncoordinated users in Wi-Fi bands; Frequency reuse in cellular Standardized for Wi-Fi and cellular Narrowband PLC (3-500 kHz)

Periodic with period of half AC main freq. plus lognormal time-selective fading

– a (f) d plus additional attenuation when passing through transformers Determinism from fixed grid topology AC main power frequency

Gaussian plus non-Gaussian noise dominated by cyclostationary component Due to power electronics and uncoordinated users using other standards Number of wires minus 1; G.9964 standard for broadband PLC

Outline

• • • • •

Research group Smart power grids Powerline noise Cyclostationary Gaussian mixture Testbeds Conclusion

IEEE Signal Processing Magazine

Special Issue on Signal Processing Techniques for the Smart Grid, September 2012. 12

Types of Powerline Noise

Background Noise

-50 -100 -150 0 100 200 300 Frequency (kHz) 400 Spectrally shaped noise with 1/

spectral decay Superposition of low intensity noise sources 500 Present in all PLC

Cyclostationary Noise

Period is synchronous to half of the AC cycle Switching power supplies and rectifiers Dominant in Narrowband PLC

Impulsive Noise

time Random impulsive bursts Circuit transient noise and uncoordinated interference Dominant in Broadband PLC 13

Cyclostationary Noise in Narrowband PLC

Medium Voltage Site Low Voltage Site Field measurements collected jointly with Aclara and Texas Instruments near St. Louis, MO USA 14

•

Cyclostationary Noise Modeling

Linear periodically time-varying system model



R N H



R N H M H i N

- Linear time invariant filter - Period in samples o o Period (half of the AC cycle) is partitioned into

segments Noise within each segment is stationary, i.e. modeled by an LTI system Segment: 1 2 3 15

•

Cyclostationary Noise Model Fitting

M = 3 segments captures temporal-spectral cyclostationarity

Measurement data Noise synthesized from model

Proposed TI-Aclara-UT model adopted in IEEE P1901.2 narrowband PLC standard 16

Asynchronous Noise Modeling

Wireless Emissions Uncoordinated Meters (coexistence) Total interference at receiver: Interference from source i 17

•

Asynchronous Noise Modeling

Aggregate interference from multiple sources Dominant interference source Impulse rate l Impulse duration m Ex. Rural areas, industrial areas with heavy machinery Homogeneous network l

= l, m

= m , g(

d i

) = g Ex. Semi-urban areas, apartment complexes General (heterogeneous) network l

, m

, g(

d i

) = g i Ex. Dense urban and commercial settings Middleton class A Middleton class A Gaussian mixture model 18

Asynchronous Noise Model Fitting

Homogeneous PLC Network General PLC Network

Tail probabilities (which direct relate to communication performance) Middleton Class A is special case of Gaussian mixture model (GMM) 19

Outline

• • • • •

Research group Smart power grids Powerline noise Cyclostationary Gaussian mixture Testbeds Conclusion

IEEE Signal Processing Magazine

Special Issue on Signal Processing Techniques for the Smart Grid, September 2012. 20

Our PLC Testbeds

• Quantify application performance vs. complexity tradeoffs Provide suite of user-configurable algorithms and system settings Display statistics of communication performance • 1x1 PLC testbeds (completed) TI PRIME modems (testbed #1) and Freescale G3 modems (testbed #2) Adaptive signal processing algorithms for bit loading and interference mitigation Goal: Improve communication performance 2-3x on indoor power lines • 2x2 PLC testbed (on-going) Use one phase, neutral and ground for 2 x 2 differential signaling Extend our 2 x 2 real-time DSL testbed (deployed in field by oil & gas company) Adaptive signal processing algorithms for crosstalk cancellation Goal: Improve communication performance by another 2x on indoor power lines 21

1 x1 PLC Testbed #1

• • •

Hardware

National Instruments (NI) controllers stream data NI cards generates/receives analog signals Texas Instruments (TI) analog front end couples to power line • •

Software

NI LabVIEW Real-Time system runs transceiver algorithms Desktop PC running LabVIEW is used as an input and visualization tool to display important system parameters.

1x1 Testbed 22

OFDM Systems in Impulsive Noise

• FFT spreads impulsive energy over all tones • SNR in each tone is decreased which increases symbol error rate Many narrowband PLC systems operate over -5 dB to 5 dB in SNR Data subchannels/tones carry same number of bits (1-4) in current standards 3 dB SNR gain could increase one bit/subchannel for same symbol error rate 23

Parametric vs. Nonparametric Noise Mitigation

Parametric Nonparametric Must build a statistical model of the noise Requires training data to compute model parameters Degrades in performance due to model mismatch Has high complexity when receiving message data Yes Yes Yes No No No No Yes 24

• •

Proposed Non-Parametric 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 additive noise (Gaussian + impulsive) time SNR gain vs. conventional OFDM systems at symbol error rate 10 -4 Complex OFDM, 128-point FFT, QPSK, data tones 33-104, rate ½ conv. code Gaussian mixture model w/ 3 terms; Middleton Class A with A = 0.1 and  = 0.01

6 dB SNR gain could mean +2 bits/tone

System

Uncoded Coded

Noise

GMM MCA GMM MCA

SBL w/ null tones

8 dB 6 dB 2 dB 1.75 dB

SBL w/ all tones

10 dB 7 dB 7 dB 6.75 dB

SBL w/ decision feedback

9 dB 8.75 dB 25

Communication Performance w/o Error Correction

Gaussian mixture model noise Non-parametric methods in blue Parametric methods in red NSI CS+LS: [Caire08] MMSE: [Haring02] SBL: [Lin11] Proposed 26

Communication Performance w/ Error Correction

Proposed NSI NSI Non-parametric methods in blue Parametric methods in red Gaussian mixture model noise 27

Exploiting Sparsity in Time Domain Reprise

• Time-domain block interleaved OFDM (TDI-OFDM 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

FPGA Test System for G3 PLC Algorithms

NI PXIe-7965R (Virtex 5) NI PXIe-1082 Real-time host tone map: tone

(kHz) 0 0 data 23 35.94

58 90.63

127 199.2

FPGA Timing/Resource Utilization

• • • • • • • Parametric Approximate Message Passing (AMP) mitigation method Base logic clock = 40 MHz, most data streams 16 bits wide Execution time: 5 iterations × 4776 cycles/iteration = 23880 cycles Supports streaming operation at 400 kS/s (G3 sample rate) without AMP with AMP Can recover up to 8 dB SNR in impulsive noise environments 100x reduction in average bit error rate using QPSK and 40 dB impulses with 3% probability Preliminary resource utilization:

Possible to exploit more parallelism for higher throughput

(steps 1-4 of AMP)

Conclusion

• • • • • PLC systems are interference limited Statistical models for interference Cyclostationary models synchronous with zero crossings of AC cycle Gaussian mixture model for asynchronous noise Interference mitigation Non-parametric sparse Bayesian learning algorithms do not map well to FPGAs Parametric distributed approximate message algorithms map well to FPGAs Testbeds Project Web site: http://users.ece.utexas.edu/~bevans/projects/plc/index.html

• • • • • • • • •

References

[Caire08] G. Caire, T.Y. Al-Naffouri, and A.K. Narayanan. Impulse noise cancellation in OFDM: an application of compressed sensing. Proc. IEEE Int. Symp. Information Theory, pages 1293–1297, 2008.

[Cho04] J. H. Cho. Joint transmitter and receiver optimization in additive cyclostationary noise. IEEE Trans. on Information Theory, 50(12), 2004.

[Garcia07] R. Garcia, L. Diez, J.A. Cortes, and F.J. Canete. Mitigation of cyclic short-time noise in indoor power-line channels. Proc. IEEE Int. Symp. Power Line Comm. and Its Applications, pp. 396–400, 2007.

[Haring02] J. Haring. Error Tolerant Communication over the Compound Channel. Aachen, 2002.

[Haring03] J. Haring and A. J. H. Vinck. Iterative decoding of codes over complex numbers for impulsive noise channels. IEEE Trans. on Information Theory, 49(5):1251–1260, 2003.

[Lampe11] L. Lampe. Bursty impulse noise detection by compressed sensing. Proc. IEEE Int. Symp.

Power Line Commun. and Appl., pages 29–34, 2011 [Liano11] A. Liano, A. Sendin, A. Arzuaga, and S. Santos. Quasi-synchronous noise interference can cellation techniques applied in low voltage PLC. Proc. IEEE Int. Symp. Power Line Comm. and Its Applications, 2011.

[Lin11] J. Lin, M. Nassar, and B. L. Evans, “Non-Parametric Impulsive Noise Mitigation in OFDM Systems Using Sparse Bayesian Learning”, Proc. IEEE Int. Global Comm. Conf., 2011.

[Lin12] J. Lin and B. L. Evans, “Cyclostationary Noise Mitigation in Narrowband Powerline Communications”, Proc. APSIPA Annual Summit and Conf., 2012.

• • • • • • • • •

References

[Nassar09] M. Nassar, K. Gulati, M. DeYoung, B.L. Evans, and K. Tinsley. Mitigating near-field interference in laptop embedded wireless transceivers. Journal of Signal Proc. Systems, pages 1–12, 2009.

[Nassar11] M. Nassar and B.L. Evans. Low Complexity EM-based Decoding for OFDM Systems with Impulsive Noise. In Proc. Asilomar Conf. on Sig., Systems, and Computers, 2011. .

[Nassar12] 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 Sig. Proc., pages 3089–3092, 2012.

[Nassar12mag] 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, vol. 29, no. 5, pp. 116-127, Sep. 2012.

[Nieman13] 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 Communications and Its Applications, Mar.

24-27, 2012, submitted.

[Pauli06] V. Pauli, L. Lampe, and R. Schober. ”turbo dpsk” using soft multiple-symbol differential sphere decoding. IEEE Trans. on Information Theory, 52(4):1385–1398, 2006.

[Raphaeli96] D. Raphaeli. Noncoherent coded modulation. IEEE Trans. on Comm., 44(2):172–183, 1996.

[Tipping01] M.E. Tipping. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1:211–244, 2001.

[Umehara01] D. Umehara, M. Kawai, and Y. Morihiro. Performance analysis of noncoherent coded modulation for power line communications. Proc. Int. Symp. Power Line Commun. and Its Appl., pages 291–298, 2001.

Backup Slides

•

Simulated Performance

Symbol error rate in different noise scenarios

~6dB ~8dB ~10dB ~6dB ~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 35

A Smart Grid

Power generation optimization Disturbance monitoring Communication to isolated area Load balancing Integrating alternative energy sources Smart metering Electric car charging & smart billing

Source: ETSI

Power Lines

• • Built for unidirectional energy flow Bidirectional information flow throughout smart grid will occur High Voltage (HV) 33 kV – 765 kV Low Voltage (LV) under 1 kV Transformer Medium Voltage (MV) 1 kV – 33 kV

Source: ERDF

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 38

Sources of Powerline Noise

Uncoordinated transmission Power line disturbance Electronic devices Taken from a local utility point of view 39

PLC In Different Frequency Bands

Category

Ultra Narrowband Narrowband Broadband

Band

0.3 – 3 kHz 3 – 500 kHz 1.8 – 250 MHz

Bit Rate

~100 bps

Applications

• • • Automatic meter reading Outage detection Load control ~500 kbps • • Smart metering Real-time energy management ~200 Mbps • Home area networks

Standards

N/A • • • 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).

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.

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 42

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 43

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.

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.

•

Statistical-Physical Modeling

Interference from a single source

pulses in a window of duration

(

) (

) (2) (1)

T k

Pulse emission duration

τ j

Pulse arrival time

=0 Emission duration: geometrically distributed with mean

Pulse arrivals: homogeneous Poisson point process with rate

Assuming channel between interference source and receiver has flat fading 46

Impulsive Noise Mitigation in OFDM Systems

• A linear system with Gaussian disturbance

v y

 *

FHF x



= 

 ,

( 

,  2

) Estimate the impulsive noise and remove it from the received signal

Apply standard OFDM decoder as if only AWGN were present 47

Parametric Vs. Non-Parametric Methods

• • Noise in different PLC networks has different statistical models Mitigation algorithms need to be robust in different noise scenarios Assume parameterized noise statistics Performance degradation due to model mismatch

Parametric Methods Non-Parametric Methods

Yes No Yes No Training needed Yes No 48

Non-Parametric Mitigation Using Null Tones

: Index set of null tones

F J

: DFT sub-matrix

: Impulsive noise in time domain

: 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 49

Sparse Bayesian Learning

• • Bayesian inference Sparsity promoting prior: Likelihood: Posterior probability:

y J e

| g ~

2 ~

(0, 2 ~

CN J diag

*   2

) 

) Iterative algorithm Step 1 : Maximum likelihood estimation of hyper-parameters (

2 ) Solved by expectation maximization (EM) algorithm (

is latent variable) Step 2 : Estimate

from the mean of the posterior probability, go to Step 1 50

Non-Parametric Mitigation Using All Tones

• Joint estimation of data and noise

: Index set of data tones

: Received signal in frequency domain Treat the received signal in data tones as additional hyper-parameters

z J