DNV research targets wide adoption of automated wind ...

Automating the inspection of wind turbines using drones is one area that has emerged but still needs wider acceptance by the industry. Sectors Allnews Policy&Regulation SmartMeters SmartGrid SmartCities Storage ElectricVehicles Energy&GridManagement EnergyEfficiency CustomerServices&Management IOT Cybersecurity Data&Analytics Digitalisation Distributedgeneration RenewableEnergy Newtechnology SmartWater Regions NorthAmerica Europe&UK Indiansubcontinent Asia Africa&MiddleEast Central&LatinAmerica Oceania Global Resources Webinars Magazine Articles MagazineIssues Elites Subscribe Events Engage Joinourcommunity Advertisewithus SubmitContent ContactUs Partners Becomeapartner SUBSCRIBE Search Search Search Search Sectors Allnews Policy&Regulation SmartMeters SmartGrid SmartCities Storage ElectricVehicles Energy&GridManagement EnergyEfficiency CustomerServices&Management IOT Cybersecurity Data&Analytics Digitalisation Distributedgeneration RenewableEnergy Newtechnology SmartWater Regions NorthAmerica Europe&UK Indiansubcontinent Asia Africa&MiddleEast Central&LatinAmerica Oceania Global Resources Webinars Magazine Articles MagazineIssues Elites Subscribe Events Engage Joinourcommunity Advertisewithus SubmitContent ContactUs Partners Becomeapartner SUBSCRIBE HomeIndustrySectorsData&AnalyticsDNV-ledresearchtargetswideadoptionofautomatedwindturbinesinspection IndustrySectorsData&AnalyticsDigitalisationEnergy&GridManagementRegionalNewsEurope&UKNews Facebook Twitter Linkedin Imagecredit:Stock Automatingtheinspectionofwindturbinesusingunmannedvehiclesisoneareathathasemergedoverthepastyearsbutstillneedswideracceptancebytheindustryandregulators. Withdigitalisationexpectedtoincreasethevalueofrenewableenergyprojectsforoperatorsanddevelopersthroughreal-timeoperationandmonitoringofassets,thereisaneedforthedevelopmentofindustry-acceptedpracticesandstandards. ThispushedengineeringandcertificationfirmDNV,theUniversityofBristolandtechnologycompanyPerceptualRoboticstolauncharesearchprogrammeaimedatgeneratingbroaderacceptanceofautomatedinspectionofwindturbinesusingdronesintheUKmarket. Overaperiodof12months,thethreepartieswilldevelopanautomateddataprocessingprocedureforverificationofdetectedwindturbinebladedefects.Todoso,theywillinvestigatetheautomatedverification,validation,andprocessingofinspectiondata,collectedbyautonomousdrones,toimproveinspectionqualityandperformance. Lessonslearnedfromtheresearchprojectwillbeusedtoinformtheindustryandgovernmentindevelopingandenactingregulationsthatwillenablewideracceptanceofautomatedinspectionofrenewablesassets. Haveyouread?HowdigitalisationwillboostthevalueofenergyfromsolarplantsNewpartnershiptodelivercostanddatabenefitsforwindfarmoperatorsDNVGLandNationalGriddevelopingGreatBritain’soffshorewindstrategy TheUniversityofBristolwillprovideitsVisualInformationLabandexpertsin3Dcomputervisionandimageprocessingtocreateautomatedlocalisationofinspectionimagesanddefects.PerceptualRoboticswillperformdroneinspectionsandcreateAI-basedmodelsfordefectdetection.DNVwillprovideinspectionexpertise,verifydatacollected,validatethemethodologyandperformanceoftheAIalgorithms.DatacollectedwillbeusedtoimproveanexistingDNVandIECrecommendedbestpracticesforautomatedwindturbineinspection. Theprojectwillbefundedusingagrantsecuredfromthe UKResearchandInnovation. Usingunmannedautonomousvehiclestogethigh-definitionvideosandimagesandtogeo-positionassetsthatarelocatedinextremeenvironmentshelpsimprovetheinspectionprocessesforassetownersandoperators,inturn,resultinginenhancedmanagement,operationandmaintenanceoftheseassets.Theprocessesenablequickdetectionoffaultybladesandotherequipment,promptingoperatorstoquicklyrespondtoavoidassetfailureandcontinuedgenerationofwindresultinginasecureenergysupplytomeetdemand.Inaddition,thelifespanoftheserenewablesassetsisincreasedthroughimprovedmaintenanceandthesafetyofworkersensuredwithoutthemhavingtovisittheharshanddangerousenvironmentsinperson. Dr.ElizabethTraiger,aDNVseniorresearcherindigitalassurancesaid:“Withmanyinspectionsstillbeingcarriedoutmanually,visualinspectionofoffshorewindturbines,isexpensive,labour-intensive,andhazardous.Automaticvisualinspectionscanaddresstheseissues.  “Thiscollaborationwilldevelopanddemonstrateanautomatedprocessingpipelinealongsideageneralframeworkwiththeaimofgeneratingbroaderacceptanceacrosstheindustryandinformingfutureregulation.Thisprojectshouldprovideastepping-stonetothegrowthoftheautomatedinspectionindustry.”  PierreCSames,groupresearchanddevelopmentdirectoratDNV,adds:“Withthenumberofinstalledwindturbinesworldwideincreasing,includingthoseinremoteandharshenvironments,thevolumeofinspectiondatacollectedisquicklyoutpacing thecapacityofskilledinspectors whocancompetentlyreviewit.Thisresearchprojectwilldevelopmeanstotacklethischallengethroughmachinelearningalgorithmsandprocessautomation.”  RELATEDARTICLESMOREFROMAUTHOR Everydropcounts:MaddalenaS.P.Aembracessustainability ConnectivitymodulesselectedforTaipower’sAMIproject Invenergytosupportrenewablesgrowthwith$3bnBlackstoneinvestment Trendingthisweek LATESTFEATURE Ed’snote:ThebirthofEUenergydiplomacy Jan11,2022 Emergingclimate-friendlytechnologiesfortheenergysector Jan7,2022 Ayearinblockchain–what’scomingfortheenergysector... 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