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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 bevans@ece.utexas.edu http://www.ece.utexas.edu/~bevans/projects/plc May 3, 2013

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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 LinPh.D. (May 2014) Summer 2013 intern at TI Mr. Yousof MortazaviPh.D. (Dec. 2013) Mr. Marcel NassarPh.D. (Aug. 2013) Defended PhD April 15, 2013 Mr. Karl NiemanPh.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

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Task Deliverables 2 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

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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) 3 Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion

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Central power plant Wind farm Houses Offices HV-MV Transformer Industrial plant Utility control center Integrating distributed energy resources Smart meters Automated control for smart appliances Grid status monitoring Device-specific billing 4 High Voltage (HV) 33 kV – 765 kV Medium Voltage (MV) 1 kV – 33 kV Smart Grid Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion

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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 Inform customer 5 Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA Enabled by smart meter communications ISTOCKPHOTO.COM/© SIGAL SUHLER MORAN Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion

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Local utility MV-LV transformer Smart meters Data concentrator Home area data networks connect appliances, EV charger and smart meter via powerline or wireless links Smart meter communications between smart meters and data concentrator via powerline or wireless links Communication backhaul carries traffic between concentrator and utility on wired or wireless links 6 Low voltage (LV) under 1 kV Smart Meter Communications Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion

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Use orthogonal frequency division multiplexing (OFDM) Communication challenges oCoChannel distortion oNoNon-Gaussian noise Powerline Communications (PLC) CategoriesBandBit RatesCoverageEnablesStandards Narrowband 3-500 kHz ~500 kbps Multi- kilometer Smart meter communication (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 7 Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion

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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 OFDM Systems in Impulsive Noise 8 Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion

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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) Solution #2: Joint transmitter-receiver design 9 Parametric MethodsNonparametric Methods Listen to environmentNo training necessary Find model parametersLearn statistical model from communication signal structure Use model to mitigate noiseExploit sparsity to mitigate noise Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion

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Narrowband PLC Impulsive Noise Cyclostationary NoiseAsynchronous Noise Example: rectified power suppliesExample: uncoordinated interference 10 Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Increases with widespread deployment Dominant in outdoor PLC Rx Receiver

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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 SNR gain vs. conventional OFDM systems at symbol error rate 10 -4 Complex, 128-point FFT, QPSK, data tones 33-104, rate ½ conv. code Asynchronous Gaussian mixture model and Middleton Class A noise 11 SystemNoise SBL w/ null tones SBL w/ all tones SBL w/ decision feedback Uncoded GMM8 dB10 dB- MCA6 dB7 dB- Coded GMM2 dB7 dB9 dB MCA1.75 dB6.75 dB8.75 dB time Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion

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Time Domain Interleaving 12 Bursts span consecutive OFDM symbols Bursts spread over many OFDM symbols Interleave Coded performance in cyclostationary noise Complex OFDM, 128-point FFT, QPSK, data tones 33-104, rate ½ conv. code Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion

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Time-Domain Interleaving 13 Burst duty cycle 10%Burst duty cycle 30% Time-domain interleaving over an AC cycle Coded performance in cyclostationary noise Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Current PLC standards use frequency-domain interleaving (FDI)

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Testbed #1 HardwareSoftware NI x86 controllers stream data NI cards generates/receives analog signals TI front end couples to power line Transceiver algorithms in C on x86 Desktop LabVIEW configures system and visualizes results 14 1x1 Testbed Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Adaptive signal processing algorithms for bit loading and interference mitigation

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G3 link using two Freescale PLC modems Freescale software tools allow frame-by-frame analysis Test setup allows synchronous noise injection into power line Testbed #2: Noise Playback/Analysis Freescale PLC G3-OFDM Modem Freescale PLC Testbed One modem to sample powerline noise in field Collected 16k 16-bit 400 kS/s at each location Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion 15

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Analyzed cyclic properties of PLC noise measurements Developed cyclic bit loading method for transmitter 1.Receiver measures noise power over half AC cycle 2.Feedback modulation map to transmitter 3.Allocate more bits in higher SNR subchannels Testbed #2: Cyclic Power Line Noise 2x increase in bit rate Won Best Paper Award at ISPLC Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion 16

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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 conventional receiverwith AMP Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion 17 UtilizationTrans.Rec.AMP+Eq FPGA123 total slices32.6%64.0%94.2% slice reg.15.8%39.3%59.0% slice LUTs17.6%42.4%71.4% DSP48s2.0%7.3%27.3% blockRAMs7.8%18.4%29.1%

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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 18 Frequency response of a direct channel Crosstalk highly correlated with direct channel response Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion

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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 19 http://users.ece.utexas.edu/~bevans/projects/plc/index.html Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion

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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. 20

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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 Message- Passing 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 21

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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-11- 0134-05-PHM5. Adopted as reference noise model in Oct. 2012 ballot. 22

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Thank you for your attention… Questions? 23

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Backup Slides 24

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

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Comparison of Wireless & PLC Systems 26 Wireless Communications Narrowband PLC (3-500 kHz) Time selectivityTime-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 – /2 where is propagation constant e – (f) d plus additional attenuation when passing through transformers PropagationDynamically changingDeterminism from fixed grid topology SynchronizationVariesAC 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 MIMOStandardized for Wi-Fi and cellular Number of wires minus 1; G.9964 standard for broadband PLC

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PLC Noise Scenarios 27 Background NoiseCyclostationary Noise Asynchronous Impulsive Noise Spectrally shaped noise Decreases with frequency Superposition of lower- intensity 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 time

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Cyclostationary Noise 28 Noise Sources Noise Trace

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Uncoordinated Interference Results 29 General PLC NetworkHomogeneous PLC Network

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Cyclostationary Noise Modeling 30 Measurement data from UT/TI field trial Cyclostationary Gaussian Model [Katayama06] Proposed model uses three filters [Nassar12] Adopted by IEEE P1901.2 narrowband PLC standard Period is one half of an AC cycle Demux s[k] is zero-mean Gaussian noise

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Asynchronous Noise Modeling 31 Ex. Rural areas, industrial areas w/ heavy machinery Dominant Interference Source Middleton Class A Distribution [Nassar11] Homogeneous PLC Network Ex. Semi-urban areas, apartment complexes General PLC Network Ex. Dense urban and commercial settings Gaussian Mixture Model [Nassar11] Middleton Class A Distribution [Nassar11] Middleton Class A is a special case of the Gaussian Mixture Model. Impulse rate Impulse duration i i d i i i d i

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Parametric vs. Nonparametric Methods ParametricNonparametric Must build a statistical model of the noise YesNo Requires training data to compute model parameters YesNo Degrades in performance due to model mismatch YesNo Has high complexity when receiving message data NoYes 32

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Sparse in time domain 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 Asynchronous Noise 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.

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Performance w/o Error Correction CS+LS: [Caire08] MMSE: [Haring02] SBL: [Lin11] NSI Gaussian mixture model noise 34 Proposed Non-parametric methods in blue Parametric methods in red

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Performance w/ Error Correction NSI Gaussian mixture model noise 35 NSI Proposed Non-parametric methods in blue Parametric methods in red

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Power Line Noise at Residential Site complex spectrum f = 30-120 kHz narrowband f = 140 kHz frequency sweep f = 170 kHz 36

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Analysis of Residential Noise though spectrally complex, many components have strong stationarity at 120 Hz 37

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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 38

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Message-Passing OFDM Receiver RT controller LabVIEW RT data symbol generation FlexRIO FPGA Module 1 (G3TX) LabVIEW DSP Design Module data and reference symbol interleave reference symbol LUT 43.2 kSps 8.6 kSps zero padding (null tones) generate complex conjugate pair 103.6 kSps 256 IFFT w/ 22 CP insertion 368.3 kSps NI 5781 16-bit DAC 10 MSps RT controller LabVIEW RT BER/SNR calculation w/ and w/o AMP FlexRIO FPGA Module 2 (G3RX) LabVIEW DSP Design Module NI 5781 14-bit ADC sample rate conversion 10 MSps400 kSps time and frequency offset correction 400 kSps 256 FFT w/ 22 CP removal 368.3 kSps FlexRIO FPGA Module 3 (AMPEQ) LabVIEW DSP Design Module null tone and active tone separation 184.2 kSps 51.8 kSps ZF channel estimation/ equalization AMP noise estimate Subtract noise estimate from active tones data and reference symbol de- interleave 51.8 kSps 8.6 kSps Host Computer LabVIEW 43.1 kSps sample rate conversion 400 kSps 51.8 kSps 256 FFT, tone select 51.8 kSps 368.3 kSps testbench control/data visualization differential MCX pair Example Input Noise Resource Utilization 39

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