一種面向微電網(wǎng)中無線傳感器網(wǎng)絡(luò)的高能效壓縮感知方法_第1頁
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一種面向微電網(wǎng)中無線傳感器網(wǎng)絡(luò)的高能效壓縮感知方法Title:AnEnergy-efficientCompressedSensingMethodforWirelessSensorNetworksinMicrogridsAbstract:WirelessSensorNetworks(WSNs)playacrucialroleinmonitoringandcontrollingmicrogrids,whicharedecentralizedpowersystemscomprisingdistributedenergyresources.However,theenergyconstraintsandlimitedcapacityofWSNnodesmakeitchallengingtotransmitthelargeamountofdatacollectedinmicrogridapplications.Toaddressthisissue,thispaperproposesanenergy-efficientcompressedsensingmethodforWSNsinmicrogrids.Theproposedmethodleveragesthespatialandtemporalcorrelationofthedatatoreducethesensingandcommunicationenergyconsumption,whilepreservingtheaccuracyofmonitoringandcontroltasks.Experimentalresultsdemonstratethattheproposedmethodachievessignificantenergysavingscomparedtotraditionaldataacquisitionmethods.1.IntroductionMicrogridshavegainedsignificantattentionduetotheirpotentialforimprovingreliability,efficiency,andsustainabilityofpowerdistribution.WSNshavebeenwidelyusedinmicrogridapplicationsforreal-timemonitoringandcontrol.However,thelarge-scaledeploymentofWSNnodesinmicrogridsposessignificantchallengesintermsofenergyconsumptionandnetworkcapacity.Thispaperaimstoaddressthesechallengesbyproposinganenergy-efficientcompressedsensingmethodforWSNsinmicrogrids.2.BackgroundCompressedSensing(CS)isapromisingtechniquethatenablesefficientdataacquisitionandtransmissioninWSNs.CSleveragesthesparsityofthedatatorecovertheoriginalsignalaccuratelyfromareducednumberofmeasurements.Byreducingthenumberoftransmitteddatasamples,CScansignificantlyreduceenergyconsumptioninWSNs.3.Energy-efficientCompressedSensingMethodTheproposedmethodexploitsthespatialandtemporalcorrelationofthedatainmicrogridstoreduceenergyconsumptionwhilemaintainingtherequiredaccuracyofmonitoringandcontroltasks.Themethodconsistsofthefollowingsteps:a)DataAcquisition:WSNnodescollectdatafromvarioussensorsinthemicrogrid.Toreducethenumberofmeasurements,theproposedmethodleveragesthespatialcorrelationamongneighboringnodes.Eachnodeonlysamplesandtransmitsthedifferencebetweenitsmeasuredvalueandtheaveragevalueofitsneighbors.b)DataCompression:Thecollecteddataissparseinthetemporaldomainduetotheslowvariationsofmicrogridparameters.Byexploitingthissparsity,theproposedmethodappliesacompressivesamplingalgorithmtofurtherreducethedatasize.c)DataReconstruction:Atthesinknode,thecompressedmeasurementsarereconstructedintotheoriginalsignalusingasparserecoveryalgorithm.Thereconstructeddatacanthenbeusedformonitoringandcontroltasksinthemicrogrid.4.ExperimentalResultsToevaluatetheperformanceoftheproposedmethod,experimentsareconductedusingarealisticmicrogridtestbed.TheEnergyConsumptionIndex(ECI)isusedtomeasuretheenergyefficiencyoftheproposedmethodcomparedtotraditionaldataacquisitionmethods.Experimentalresultsdemonstratethattheproposedmethodachievessubstantialenergysavingswithoutsacrificingtheaccuracyofmonitoringandcontroltasks.5.DiscussionandFutureWorkTheproposedmethodoffersanenergy-efficientsolutionforWSNsinmicrogridsbyleveragingthesparsityandcorrelationofdata.However,thereareseveralareasforfurtherimprovementandresearch.Futureworkcouldfocusonoptimizingthesparsity-basedreconstructionalgorithmstoimprovetheaccuracyofdatarecovery.Additionally,theproposedmethodcouldbeextendedtoconsiderdynamicnetworkconditionsandadaptivelyadjustthecompressionratiobasedontheavailableenergyandcommunicationcapacity.6.ConclusionThispaperpresentedanenergy-efficientcompressedsensingmethodforWSNsinmicrogrids,aimingtoreducetheenergyconsumptionandnetworkcapacityrequirements.Theproposedmethodexploitsthespatialandtemporalcorrelationofdataandachievessignificantenergysavingscompared

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