Report

Seminar at the American University of Beirut Co-sponsored by the local IEEE chapter 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 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 Outline • Research group overview • Smart power grids • Powerline noise Cyclostationary Gaussian mixture • Testbed • Conclusion My visits to Lebanon 1 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 wncg.org 2 Research Group – Completed Projects 20 PhD and 9 MS alumni System SW release Prototype Companies Matlab DSP/C Freescale, TI MIMO testbed LabVIEW LabVIEW/PXI Oil&Gas Wimax/LTE resource allocation LabVIEW DSP/C Freescale, TI Camera image acquisition Matlab DSP/C Intel, Ricoh Display image halftoning Matlab C HP, Xerox video halftoning Matlab fixed point conv. Matlab FPGA Intel, NI distributed comp. Linux/C++ Navy sonar Navy, NI ADSL EDA tools Contribution equalization DSP Digital Signal Processor MIMO Multi-Input Multi-Output LTE PXI Qualcomm Long-Term Evolution (cellular) PCI Extensions for Instrumentation 3 Research Group – Current Projects 6 PhD and 4 MS students System Powerline comm. Contributions noise reduction; MIMO testbed SW release Prototype Companies LabVIEW LabVIEW / PXI chassis Freescale, IBM, TI Matlab FPGA Intel, NI Wimax, LTE interference & WiFi reduction time-based ADC IBM 45nm Underwater space-time methods; comm. MIMO testbed Matlab Lake Travis testbed Navy Cell phone camera reducing rolling shutter artifacts Matlab Android TI EDA Tools reliability patterns MIMO Multi-Input Multi-Output NI PXI PCI Extensions for Instrumentation 4 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 Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA 5 A Smart Grid Communication to isolated area Power generation optimization Integrating alternative energy sources Load balancing Disturbance monitoring Smart metering Electric car charging & smart billing Source: ETSI 6 Power Lines • Built for unidirectional energy flow • Bidirectional information flow throughout smart grid will occur Low Voltage (LV) under 1 kV High Voltage (HV) 33 kV – 765 kV Medium Voltage (MV) 1 kV – 33 kV Transformer Source: ERDF 7 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 Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA 8 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 9 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 10 PLC In Different Frequency Bands Category Band Ultra Narrowband Narrowband Broadband Bit Rate Applications Standards 0.3 – 3 kHz ~100 bps • Automatic meter reading • Outage detection • Load control N/A 3 – 500 kHz • Smart metering ~500 kbps • Real-time energy management 1.8 – 250 ~200 Mbps • Home area networks MHz • 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). 11 Comparison Between Wireless and PLC Systems Wireless Communications Narrowband PLC (3-500 kHz) Time selectivity Due to node mobility From random load variations due to switching activity Time-varying stochastic model Doppler spectrum 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 Additive noise Assumed stationary and Gaussian non-Gaussian and impulsive with dominant cyclostationary component Propagation Dynamically changing Determinism from fixed grid topology Interference limited In Wi-Fi deployments and increasing in cellular Increasing due to uncoordinated users using different standards Standardized for Wi-Fi and cellular Order of #wires minus 1; G.9964 standard for broadband PLC Difficult across network AC main frequency makes simpler MIMO Synchronization 12 Physical Layer Parameters for OFDM Narrowband PLC Standards 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. 13 Sources of Powerline Noise Uncoordinated transmission Power line disturbance Electronic devices Taken from a local utility point of view 14 Types of Powerline Noise Background Noise Cyclostationary Noise Impulsive Noise -50 -100 -150 0 100 200 300 Frequency (kHz) 400 time 500 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 15 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 16 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 Cyclostationary Noise: Field Measurement Medium Voltage Site Low Voltage Site Data collected jointly with Aclara and Texas Instruments near St. Louis, MO USA 18 Noise Modeling • Linear periodically time-varying (LPTV) system model H1 vR H2 N n RN … HM Hi - Linear time invariant filter N - Period in samples o A period is partitioned into M segments o Noise within each segment is stationary, i.e. modeled by an LTI system Segment: 1 23 19 Model Fitting • LPTV model (M = 3) captures temporal-spectral cyclostationarity Measurement data Noise synthesized from model The proposed TI-Aclara-UT model was adopted in the IEEE P1901.2 narrowband PLC standard 20 A Lebanese Interlude Ghine Jbeil/Byblos Not shown: Baalbek, Beirut, Tripoli, Tyre, Zahle, and other great places Baruk Cedars Beiteddine Sidon Jezzine 21 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 Sources of Impulsive Noise Wireless Emissions In-home PLC Switching Transients Total interference at receiver: Uncoordinated Meters (coexistence) Interference from source i 23 Statistical-Physical Modeling • Interference from a single source Noise envelope k pulses in a window of duration T (k) (j) Tk Pulse emission duration (1) (2) τj Pulse arrival time t=0 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 24 Statistical-Physical Modeling (cont.) • Aggregate interference from multiple sources Dominant interference source Impulse rate Impulse duration Ex. Rural areas, industrial areas with heavy machinery Homogeneous network i , i , (di) Ex. Semi-urban areas, apartment complexes General (heterogeneous) network i, i, (di) i Ex. Dense urban and commercial settings Middleton class A A A (d ) E[h 2 B 2 ] 2 Middleton class A A M A E[h 2 B 2 ] 2M Gaussian mixture model π and σ2 in [3] 25 Model Fitting: Tail Probability Homogeneous PLC Network General PLC Network Middleton Class A model is a special case of the Gaussian mixture model (GMM) 26 OFDM Systems in Impulsive Noise • FFT spreads out impulsive energy across all tones SNR in each tone is decreased Receiver performance degrades 27 Impulsive Noise Mitigation in OFDM Systems • A linear system with Gaussian disturbance v y Fe FHF * x Fn Fe v, g v ~ CN (x, 2 I ) Estimate the impulsive noise and remove it from the received signal yˆ y Feˆ x g Apply standard OFDM decoder as if only AWGN were present 28 Parametric Vs. Non-Parametric Methods • Noise in different PLC networks has different statistical models • Mitigation algorithms need to be robust in different noise scenarios Parametric Methods Non-Parametric Methods Assume parameterized noise statistics Yes No Performance degradation due to model mismatch Yes No Training needed Yes No 29 Non-Parametric Mitigation Using Null Tones J : Index set of null tones FJ : DFT sub-matrix e: Impulsive noise in time domain g: 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 30 Sparse Bayesian Learning • Bayesian inference Sparsity promoting prior: Likelihood: Posterior probability: e | ~ CN (0, ), diag ( ) yJ | , 2 ~ CN (0, FF * 2 I ) e | yJ ; , 2 ~ CN (, e ) • Iterative algorithm Step 1: Maximum likelihood estimation of hyper-parameters (γ, σ2) 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 31 Non-Parametric Mitigation Using All Tones • Joint estimation of data and noise J : Index set of data tones z : Received signal in frequency domain Treat the received signal in data tones as additional hyper-parameters Estimate of zJ is sent to standard OFDM equalizer and symbol detector 32 Simulated Communication Performance • Interference in time domain -1 10 ~10dB time 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 10100x for same SNR Symbol Error Rate • Learn statistical model ~6dB -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. 33 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 Our PLC Testbed Hardware Software • National Instruments (NI) controllers stream • NI LabVIEW Real-Time system runs data transceiver algorithms • NI cards generates/receives analog signals • Desktop PC running LabVIEW is used • Texas Instruments (TI) analog front end as an input and visualization tool to couples to power line display important system parameters. 1x1 Testbed 35 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 Thank you … 37 Simulated Performance • Symbol error rate in different noise scenarios ~10dB ~6dB ~6dB ~8dB ~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 38