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 bevans@ece.utexas.edu 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