Presentation on theme: "Principal Investigator:"— Presentation transcript:
0POWERLINE COMMUNICATIONS FOR ENABLING SMART GRID APPLICATIONS Task ID:Prof. Brian L. EvansWireless Networking and Communications GroupCockrell School of EngineeringThe University of Texas at AustinMay 3, 2013
1Principal Investigator: Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionTask Description:Improve powerline communication (PLC) bit rates for monitoring/controlling applications for residential and commercial energy usesAnticipated Results:Adaptive methods and real-time prototypes to increase bit rates in PLC networksPrincipal Investigator:Prof. Brian L. Evans, The University of Texas at AustinCurrent Students (with expected graduation dates):Ms. Jing Lin Ph.D. (May 2014) Summer 2013 intern at TIMr. Yousof Mortazavi Ph.D. (Dec. 2013)Mr. Marcel Nassar Ph.D. (Aug. 2013) Defended PhD April 15, 2013Mr. Karl Nieman Ph.D. (May 2015) Summer 2013 intern at FreescaleIndustrial 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
2Task Deliverables Date Tasks Dec 2010 Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionTask DeliverablesDateTasksDec 2010Uncoordinated interference in narrowband PLC: measurements, modeling, and mitigationMay 2011Testbed #1 based on TI PLC modems to investigate receiver improvementsDec 2011Narrowband PLC channel/noise: measurements/modelingMay 2012Standard-compliant receiver methods (3x bit rate increase)Dec 2012Testbed #2 based on Freescale PLC modems to investigate transmitter improvements (2x bit rate increase)On-goingTestbed #3 based on NI equipment to map noise mitigation algorithms onto FPGAsTestbed #4 for two-transmitter two-receiver (2x2) systems based on TI PLC modems to investigate scalability
3Recent Project Highlights Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionRecent Project HighlightsPaper in Smart Grid Special Issue (Sep. 2012)IEEE Signal Processing Magazine (impact factor 4.066)Paper on channel impairments, noise, and standardsCo-authored with Dr. Anand Dabak (TI) and Dr. Il Han Kim (TI)Channel Model Adopted (Oct. 2012)Reference model for IEEE 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 ApplicationsCo-authored with Dr. Khurram Waheed (Freescale)
4Smart Grid Wind farm Central power plant HV-MV Transformer Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionSmart GridWind farmCentral power plantHV-MV TransformerGrid status monitoringUtility control centerSmart metersIntegrating distributed energy resourcesHousesOfficesTraditional power grids have been coupled with communication networks today, leading to so-called smart grids. A smart grid enables information flow among various components of the grid, ranging from power plants to distributed energy resources, and from local utilities to residential and commercial customers. The purpose is to better monitor and control power generation and consumption. For example, distributed energy resources, such as solar power, fuel power and other personally-owned energy storage, can be integrated to the grid to provide low-cost or standby energy during peak hours. Transducers deployed over the grids are used to collect measurement data for grid status estimation and outage detection. Smart meters can be used for time-dependent pricing, which motivates customers to scale back their energy usage during peak hours, and also device-specific billing, so that the house owner doesn’t have be pay electric bills for charging his friend’s electric vehicle. In addition, smart buildings and smart homes can be energy efficient with automated lights, air conditioners and other smart appliances.Device-specific billingAutomated control for smart appliancesMedium Voltage (MV) 1 kV – 33 kVHigh Voltage (HV) 33 kV – 765 kVIndustrial plant
6Smart Meter Communications Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionSmart Meter CommunicationsLocal utilityMV-LV transformerSmart metersData concentratorCommunication backhaulcarries traffic between concentrator and utility on wired or wireless linksSmart meter communicationsbetween smart meters and data concentrator via powerline or wireless linksOn the distribution side of the grids, the communications between a local utility control center and its customers plays an important role in local utility applications that I just mentioned. The local utility communicates with a number of data concentrators located at the medium-voltage lines in the US via communication backhaul. Each data concentrator is in charge of a few houses and can talk to them via the last mile communications to the smart meters. The smart meter serves as a gateway in the home area sensor networks that interconnect transducers on appliances and indoor power lines.Distances from Smart Meter to Data Concentrator: m in Europe/China/India and 3-4 km in US 3-4Low voltage (LV) under 1 kVHome area data networksconnect appliances, EV charger and smart meter via powerline or wireless links
7Powerline Communications (PLC) Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionPowerline Communications (PLC)Use orthogonal frequency division multiplexing (OFDM)Communication challengesChannel distortionNon-Gaussian noiseCategoriesBandBit RatesCoverageEnablesStandardsNarrowband3-500 kHz~500 kbpsMulti-kilometerSmart meter communication(ITU) PRIME, G3ITU-T G.hnemIEEE P1901.2BroadbandMHz~200 Mbps<1500 mHome area data networksHomePlugITU-T G.hnIEEE P1901The smart grid communications are supported by a heterogeneous set of network technologies, ranging from wireless to wireline solutions. Among the wireline alternatives, powerline communications, or PLC, have been deployed outdoor for last mile communications and indoor for home area networks. Narrowband PLC operating in the kHz band to deliver a few hundred kbps has been used for last mile communications over MV and LV lines. For home area networks, broadband PLC can provide several hundred Mbps in the MHz band. These PLC systems adopt multicarrier communications, or OFDM. (Introduce OFDM here)
8OFDM Systems in Impulsive Noise Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionOFDM Systems in Impulsive NoiseFFT in receiver spreads impulsive energy over all tonesSignal-to-noise ratio (SNR) in each subchannel decreasesNarrowband PLC systems operate -5 dB to 5 dB in SNRData subchannels carry same number of bits (1-4) in current standardsEach 3 dB increase in SNR on data subchannels could give extra bit
9Narrowband PLC Systems Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionNarrowband PLC SystemsProblem: Non-Gaussian impulsive noise is #1 limitation to communication performance yet traditional communication system design assumes additive noise is GaussianGoal: Improve comm. performance in impulsive noiseApproach: Statistical modeling of impulsive noiseSolution #1: Receiver design (standard compliant)Solution #2: Joint transmitter-receiver designParametric MethodsNonparametric MethodsListen to environmentNo training necessaryFind model parametersLearn statistical model from communication signal structureUse model to mitigate noiseExploit sparsity to mitigate noise
10Narrowband PLC Impulsive Noise Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionNarrowband PLC Impulsive NoiseCyclostationary NoiseAsynchronous NoiseExample: rectified power suppliesExample: uncoordinated interferenceRx ReceiverDominant in outdoor PLCIncreases with widespread deployment
11Non-Parametric Mitigation Methods Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionNon-Parametric Mitigation MethodsExploit sparsity of impulsive noise in time domainBuild statistical model each OFDM symbol using sparse Bayesian learning (SBL)At receiver, null tones contain only Gaussian + impulsive noiseSNR gain vs. conventional OFDM systems at symbol error rate 10-4Complex, 128-point FFT, QPSK, data tones , rate ½ conv. codeAsynchronous Gaussian mixture model and Middleton Class A noisetimeSystemNoiseSBL w/ null tonesSBL w/ all tonesSBL w/ decision feedbackUncodedGMM8 dB10 dB-MCA6 dB7 dBCoded2 dB9 dB1.75 dB6.75 dB8.75 dB
12Time Domain Interleaving Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionTime Domain InterleavingBursts span consecutive OFDM symbolsCoded performance in cyclostationary noiseInterleaveComplex OFDM, 128-point FFT, QPSK, data tones , rate ½ conv. codeBursts spread over many OFDM symbols
13Time-Domain Interleaving Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionTime-Domain InterleavingCoded performance in cyclostationary noiseBurst duty cycle 10%Burst duty cycle 30%Time-domain interleaving over an AC cycleCurrent PLC standards use frequency-domain interleaving (FDI)
14Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Adaptive signal processing algorithms for bit loading and interference mitigationHardwareSoftwareNI x86 controllers stream dataNI cards generates/receives analog signalsTI front end couples to power lineTransceiver algorithms in C on x86Desktop LabVIEW configures system and visualizes results1x1 Testbed
15Testbed #2: Noise Playback/Analysis Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionTestbed #2: Noise Playback/AnalysisG3 link using two Freescale PLC modemsFreescale software tools allow frame-by-frame analysisTest setup allows synchronous noise injection into power lineFreescale PLC G3-OFDM ModemOne modem to sample powerline noise in fieldCollected 16k 16-bit 400 kS/s at each locationFreescale PLC Testbed
16Testbed #2: Cyclic Power Line Noise Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionTestbed #2: Cyclic Power Line NoiseAnalyzed cyclic properties of PLC noise measurementsDeveloped cyclic bit loading method for transmitterReceiver measures noise power over half AC cycleFeedback modulation map to transmitterAllocate more bits in higher SNR subchannels2x increase in bit rateWon Best Paper Award at ISPLC
17Testbed #3: FPGA Implementation Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionTestbed #3: FPGA ImplementationBuilt NI/LabVIEW testbed with real-time link (G3 settings)Redesigned parametric impulsive noise mitigation algorithmConverted matrix operations to distributed calculations on scalarsBased on approximate message passing (AMP) frameworkMapped transceiver to fixed-point data/math using MatlabSynthesis: LabVIEW DSP Diagram to Xilinx Vertex 5 FPGAsReceived QPSK constellation at equalizer outputUtilizationTrans.Rec.AMP+EqFPGA123total 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%conventional receiverwith AMP
18Testbed #4: 2x2 PLC (On-Going) Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionTestbed #4: 2x2 PLC (On-Going)Goal: Improve communication performance by another 2xOne phase, neutral, ground for 2x2 differential signalingCrosstalk between two channels due to energy couplingFrequency response of a direct channelCrosstalk highly correlated with direct channel response
19Conclusion PLC systems are interference limited Task Summary | Background | Noise Modeling and Mitigation | Testbeds | ConclusionConclusionPLC systems are interference limitedStatistical models for interferenceCyclostationary model for impulsive noise synchronous to AC cycleGaussian mixture model for asynchronous impulsive noiseInterference managementCyclic bit loading to double bit rates in cyclostationary noiseTime-domain interleaving to mitigate cyclostationary noise followed by receiver impulsive noise mitigationMapping impulsive noise mitigation algorithms to FPGAsPoor: Non-parametric sparse Bayesian learning algorithmsGood: Parametric distributed approximate message algorithms
20Our 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 kHz Band: Channel Impairments, Noise, and Standards”, IEEE Signal Processing Magazine, Special Issue on Signal Processing Techniques for the Smart Grid, Sep Impact FactorJournal PaperJ. 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 Impact FactorConference 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.
21Our 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 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
22Our 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 ContributionA. Dabak, B. Varadrajan, I. H. Kim, M. Nassar, and G. Gregg, “Appendix for noise channel modeling for IEEE P1901.2”, IEEE P Std., June 2011, doc: 2wg PHM5. Adopted as reference noise model in Oct ballot.
25Today’s Power Grids in USA 7 large-scale power grids each managed by a regional utility company700 GW generation capacity in total for long-haul high-voltage power transmissionSynchronized independently, and exchange power via DC transfer130+ medium-scale power grids each managed by a local utilityLocal power distribution to residential, commercial and industrial customersHeavy penalties in US for blackouts (2003 legislation)Utilities generate expected energy demand plus 12%Energy demand correlated with time of dayEffect of plug-in electric vehicles (EVs) on energy demand uncertainGeneration cost 30x higher during peak times vs. normal loadTraditional ways to increase capacity to meet peak demand increaseBuild new large-scale power generation plant at cost of $1-10B if permit issuedBuild new transmission line at $0.6M/km which will take 5-10 years to completeSource: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA
26Comparison of Wireless & PLC Systems Wireless CommunicationsNarrowband PLC (3-500 kHz)Time selectivityTime-selective fading and Doppler shift (cellular)Periodic with period of half AC main freq. plus lognormal time-selective fadingPower loss vs. distance dd –n/2 where n is propagation constante – a(f) d plus additional attenuation when passing through transformersPropagationDynamically changingDeterminism from fixed grid topologySynchronizationVariesAC main power frequencyAdditive noise/ interferenceAssumed stationary and GaussianGaussian plus non-Gaussian noise dominated by cyclostationary componentAsynchronous interferenceUncoordinated users in Wi-Fi bands; Frequency reuse in cellularDue to power electronics and uncoordinated users using other standardsMIMOStandardized for Wi-Fi and cellularNumber of wires minus 1; G.9964 standard for broadband PLC
27Cyclostationary Noise Asynchronous Impulsive Noise PLC Noise ScenariosBackground NoiseCyclostationary NoiseAsynchronous Impulsive NoiseSpectrally shaped noiseDecreases with frequencySuperposition of lower-intensity sourcesIncludes narrowband interferenceCylostationary in time and frequencySynchronous and asynchronous to AC main frequencyComes from rectified and switched power supplies (synchronous), and electrical motors (asynchronous)Dominant in narrowband PLCImpulse duration from micro to millisecondRandom inter-arrival time50dB above background noiseCaused by switching transients and uncoordinated interferencePresent in narrowband and broadband PLCtime
30Cyclostationary Noise Modeling Measurement data from UT/TI field trialCyclostationary Gaussian Model [Katayama06]Proposed model uses three filters [Nassar12]DemuxPeriod is one half of an AC cycles[k] is zero-mean Gaussian noiseAdopted by IEEE P narrowband PLC standard
31Asynchronous Noise Modeling Dominant Interference SourceEx. Rural areas, industrial areas w/ heavy machineryMiddleton Class A Distribution [Nassar11]Impulse rate l Impulse duration mHomogeneous PLC Networkli = l, mi = m, g(di) = g0Ex. Semi-urban areas, apartment complexesMiddleton Class A Distribution [Nassar11]General PLC Networkli, mi, g(di) = giEx. Dense urban and commercial settingsGaussian Mixture Model [Nassar11]Middleton Class A is a special case of the Gaussian Mixture Model.
32Parametric vs. Nonparametric Methods Must build a statistical model of the noiseYesNoRequires training data to compute model parametersDegrades in performance due to model mismatchHas high complexity when receiving message dataPrior receiver methods to cope with non-Gaussian noise can be categorized as parametric and nonparametric approaches. Parametric methods assumes a statistical noise model, estimates model parameters during a training stage, and use that to mitigate noise during data transmissions. These methods generally allow low-complexity implementations during the transmission stage. However, in time-varying noise statistics, re-training is needed which introduces extra overhead. Otherwise performance degradation can be expected due to model mismatch. Nonparametric methods, on the other hand, require no additional training since they don’t assume any noise models. Instead they denoise the received signal by exploiting certain sparsity structure of the noise. These methods are more robust in various noise environments since it doesn’t rely on statistical models but the sparse structure of the noise. However, the denoising algorithms are generally of high computational complexity.
33Asynchronous Noise Sparse in time domain Learn statistical model Use sparse Bayesian learning (SBL)Exploit sparsity in time domain [Lin11]SNR gain of 6-10 dBIncreases 2-3 bits per tone for same error rate - OR -Decreases bit error rate by x for same SNRtime~10dB~6dBTransmission places 0-3 bits at each tone (frequency). At receiver, null tone carries 0 bits and only contains impulsive noise.
34Performance w/o Error Correction NSIGaussian mixture model noiseNon-parametric methods in blueParametric methods in redProposedCS+LS: [Caire08] MMSE: [Haring02] SBL: [Lin11]
35Performance w/ Error Correction NSIProposedNon-parametric methods in blueParametric methods in redNSIGaussian mixture model noise
36Power Line Noise at Residential Site frequency sweep f = 170 kHznarrowband f = 140 kHzcomplex spectrum f = kHz
37Analysis of Residential Noise though spectrally complex, many components have strong stationarity at 120 Hz
38Testbed #1 Quantify application performance vs. complexity tradeoffs Extend our real-time DSL testbed (deployed in field)Integrate ideas from multiple narrowband PLC standardsProvide suite of user-configurable algorithms and system settingsDisplay statistics of communication performanceInvestigateAdaptive signal processing algorithmsImproved communication performance 2-3x
39Message-Passing OFDM Receiver RT controllerLabVIEW RTdata symbol generationFlexRIO FPGA Module 1 (G3TX)LabVIEW DSP Design Moduledata and reference symbol interleavereference symbol LUT43.2 kSps8.6 kSpszero padding (null tones)generate complex conjugate pair103.6 kSps256 IFFT w/ 22 CP insertion368.3 kSpsNI 578116-bit DAC10 MSpsBER/SNR calculation w/ and w/o AMPFlexRIO FPGA Module 2 (G3RX)14-bit ADCsample rate conversion400 kSpstime and frequency offset correction256 FFT w/ 22 CP removalFlexRIO FPGA Module 3 (AMPEQ)null tone and active tone separation184.2 kSps51.8 kSpsZF channel estimation/ equalizationAMP noise estimateSubtract noise estimate from active tonesdata and reference symbol de- interleaveHost ComputerLabVIEW43.1 kSps256 FFT, tone selecttestbench control/data visualizationdifferential MCX pairExample Input NoiseResource Utilization