1. 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 Seminar at the American University of Beirut Co-sponsored by the local IEEE chapter 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

2. Outline Research group overview Smart power grids Powerline noise Cyclostationary Gaussian mixture Testbed Conclusion 1 My visits to Lebanon (x2)

3. 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 2 wncg.org

4. Research Group – Completed Projects SystemContributionSW releasePrototypeCompanies ADSLequalizationMatlabDSP/CFreescale, TI MIMO testbedLabVIEWLabVIEW/PXIOil&Gas Wimax/LTEresource allocationLabVIEWDSP/CFreescale, TI Cameraimage acquisitionMatlabDSP/CIntel, Ricoh Displayimage halftoningMatlabCHP, Xerox video halftoningMatlabQualcomm EDA toolsfixed point conv.MatlabFPGAIntel, NI distributed comp.Linux/C++Navy sonarNavy, NI DSP Digital Signal Processor LTE Long-Term Evolution (cellular) MIMO Multi-Input Multi-Output PXI PCI Extensions for Instrumentation 20 PhD and 9 MS alumni 3

5. Research Group – Current Projects SystemContributionsSW releasePrototypeCompanies Powerline comm. noise reduction; MIMO testbed LabVIEWLabVIEW / PXI chassis Freescale, IBM, TI Wimax, LTE & WiFi interference reduction MatlabFPGAIntel, NI time-based ADCIBM 45nm Underwater comm. space-time methods; MIMO testbed MatlabLake Travis testbed Navy Cell phone camera reducing rolling shutter artifacts MatlabAndroidTI EDA Toolsreliability patternsNI 6 PhD and 4 MS students MIMO Multi-Input Multi-Output PXI PCI Extensions for Instrumentation 4

6. 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 5 Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA

7. A Smart Grid Electric car charging & smart billing Communication to isolated area Power generation optimization Disturbance monitoring Disturbance monitoring Smart metering Integrating alternative energy sources Source: ETSI Load balancing 6

8. Built for unidirectional energy flow Bidirectional information flow throughout smart grid will occur Power Lines Medium Voltage (MV) 1 kV – 33 kV Low Voltage (LV) under 1 kV High Voltage (HV) 33 kV – 765 kV Source: ERDF Transformer 7

9. 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 8 Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA

10. 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) 9 Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA

11. Local Utility Powerline Communications (PLC) 10 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

12. PLC In Different Frequency Bands CategoryBandBit RateApplicationsStandards Ultra Narrowband 0.3 – 3 kHz ~100 bps Automatic meter reading Outage detection Load control N/A Narrowband 3 – 500 kHz ~500 kbps Smart metering Real-time energy management PRIME, G3 ITU-T G.hnem IEEE P1901.2 Broadband 1.8 – 250 MHz ~200 Mbps Home area networks HomePlug ITU-T G.hn IEEE P1901 11 All of the above standards are based on multicarrier communications using orthogonal frequency division multiplexing (OFDM).

13. Comparison Between Wireless and PLC Systems 12 Wireless CommunicationsNarrowband PLC (3-500 kHz) Time selectivityDue to node mobilityFrom random load variations due to switching activity Time-varying stochastic model Doppler spectrumPeriodic with period of half AC main freq. plus lognormal time-selective fading Power loss vs. distance d d – /2 where is propagation constant e –  (f) d plus additional attenuation when passing through transformers Additive noiseAssumed stationary and Gaussian non-Gaussian and impulsive with dominant cyclostationary component PropagationDynamically changingDeterminism from fixed grid topology Interference limited In Wi-Fi deployments and increasing in cellular Increasing due to uncoordinated users using different standards MIMOStandardized for Wi-Fi and cellular Order of #wires minus 1; G.9964 standard for broadband PLC SynchronizationDifficult across networkAC main frequency makes simpler

14. Physical Layer Parameters for OFDM Narrowband PLC Standards 13 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.

15. Sources of Powerline Noise Electronic devices Uncoordinated transmission Power line disturbance 14 Taken from a local utility point of view

16. Types of Powerline Noise Background NoiseCyclostationary NoiseImpulsive Noise 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 time 15

17. Performance of conventional communication system degrades in non-AWGN environment Statistical modeling of powerline noise Noise mitigation exploiting the noise model or structure Non-Gaussian Noise: Challenge to PLC Listen to the environment Estimate noise model Use model or structure to mitigate noise 16

18. 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

19. Cyclostationary Noise: Field Measurement Medium Voltage SiteLow Voltage Site Data collected jointly with Aclara and Texas Instruments near St. Louis, MO USA 18

20. Linear periodically time-varying (LPTV) system model o A period is partitioned into M segments o Noise within each segment is stationary, i.e. modeled by an LTI system Noise Modeling … H i - Linear time invariant filter N - Period in samples Segment: 1 2 3 19

21. LPTV model ( M = 3) captures temporal-spectral cyclostationarity Model Fitting Measurement dataNoise synthesized from model The proposed TI-Aclara-UT model was adopted in the IEEE P1901.2 narrowband PLC standard 20

22. A Lebanese Interlude 21 Jezzine Sidon Beiteddine Jbeil/Byblos Baruk Cedars Ghine Not shown: Baalbek, Beirut, Tripoli, Tyre, Zahle, and other great places

23. 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

24. Sources of Impulsive Noise Wireless Emissions In-home PLC Switching Transients Uncoordinated Meters (coexistence) Total interference at receiver: Interference from source i 23

25. Interference from a single source 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 Statistical-Physical Modeling t=0 Noise envelope (k)(k) (j)(j) (2) (1) k pulses in a window of duration T TkTk Pulse emission duration τjτj Pulse arrival time 24

26. Aggregate interference from multiple sources Statistical-Physical Modeling (cont.) 25 Ex. Rural areas, industrial areas with heavy machinery Dominant interference source Middleton class A Impulse rate Impulse duration  Homogeneous network Ex. Semi-urban areas, apartment complexes Middleton class A i  i  d i  General (heterogeneous) network Ex. Dense urban and commercial settings Gaussian mixture model π and σ 2 in [3] i  i  d i  

27. Model Fitting: Tail Probability Homogeneous PLC NetworkGeneral PLC Network 26 Middleton Class A model is a special case of the Gaussian mixture model (GMM)

28. FFT spreads out impulsive energy across all tones SNR in each tone is decreased Receiver performance degrades OFDM Systems in Impulsive Noise 27

29. A linear system with Gaussian disturbance Estimate the impulsive noise and remove it from the received signal Apply standard OFDM decoder as if only AWGN were present Impulsive Noise Mitigation in OFDM Systems 28

30. Noise in different PLC networks has different statistical models Mitigation algorithms need to be robust in different noise scenarios Parametric Vs. Non-Parametric Methods 29 Parametric MethodsNon-Parametric Methods Assume parameterized noise statistics YesNo Performance degradation due to model mismatch YesNo Training neededYesNo

31. 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 Non-Parametric Mitigation Using Null Tones J : Index set of null tones F J : DFT sub-matrix e : Impulsive noise in time domain g : AWGN with unknown variance 30

32. Bayesian inference Sparsity promoting prior: Likelihood: Posterior probability: Iterative algorithm Step 1: Maximum likelihood estimation of hyper-parameters ( γ, σ 2 ) 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 Sparse Bayesian Learning 31

33. Joint estimation of data and noise Treat the received signal in data tones as additional hyper-parameters Estimate of is sent to standard OFDM equalizer and symbol detector Non-Parametric Mitigation Using All Tones 32 : Index set of data tones z : Received signal in frequency domain

34. Interference in time domain Learn statistical model 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 10- 100x for same SNR Simulated Communication Performance 33 ~10dB ~6dB time Transmission places 0-3 bits at each tone (frequency). At receiver, null tone carries 0 bits and only contains impulsive noise.

35. 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

36. Our PLC Testbed 35 HardwareSoftware National Instruments (NI) controllers stream data NI cards generates/receives analog signals Texas Instruments (TI) analog front end couples to power line 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

37. 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

38. Thank you … 37

39. Symbol error rate in different noise scenarios Simulated Performance Middleton class A modelGaussian mixture model ~6dB ~8dB ~4dB 38 ~6dB ~10dB MMSE w/ (w/o) CSI: Parametric estimator assuming known (unknown) statistical parameters of noise CS+LS: A compressed sensing and least squares based algorithm