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Fault management with big data

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Fault management with big data

  1. 1. Power and productivity v Smart ( ll ids and l ncrriy Marktñls Finnish Smart Grids Highlights - _ u / V u n” - u Needs for new types of data analytics Development_ f “ T of working Business processes pmcedures - Maintenance management , i t Safety and security management v' From data to v Power quality management 'x information to New and/ or *support decision 3 intproved functicins for gmaking v operations __ _ supportingrunctrons - condition based maintenance tive power quality monitoring . ' """ "Requirementsfor ICT Infrastructure : Ommunimm - local processing, data transfer l g g data storage and - centralized processing Mining ' history data base Input data - AMR - Suhstations Lot of data is available already today, but it is not sufficiently utilized when making business decisions. For example protection and control devices create data from fault situations, which could be utilized also for other purposes besides fault clearance. Different levels of data processing Location Enterprlce / A . loud Analysis result Mumple 'T s st u upe v a . . . Multiple data Network Momtmmg sources n . . Control Center : dt/ a nced Mumme “ncnons substations Substation "Simple" functions One 'ED Protection subsmic" &Control One device Raw data One sensor Process When data analytics are taken to higher levels than traditional process level, new challenges arise. In one aspect tha data volume is reduced when already processed data from process level devices can be utilized. But the need to integrate results from multiple devices and multiple different IT systems creates new 'Big Data challenges' for a better world” r 'i TJ. . in! ! . url BšeLeNIn l Example value chain for fault management Cloud Service central cuntml customers 9 Requires new Central scAnA/ DMS Substatlon Controller "New functions" to customers 9 Business as usual š š ' Feeder IED secondary s/ s Cnnsumpttun It is important to look at data utilization holistically throughout the whole data processing chain. In SGEM programme this was done to the application area of fault management. In process level the data is needed for fault Clearance purposes. But in enterprice level the same data can be utilized for fault prediction and preventive maintenance purposes. Research pilot results In SGEM programme these data analytics were realized as cloud services. New needs for data utilization and incresing amount of open data sources require technology providers to develop more agile platforms based on cloud technology and service oriented business models. This has been successfully demonstrated within SGEM programme with few target end-user benefits: - Reduce maintenance costs: Auto-generate test reports with available process data. When the protection has been successfully reacting to real network faults, there is no need for additional tests o Predict faults: React to small disturbances before a permanent fault happens. When fault location and correlation to weather data is evaluated already to small disturbances, utility can react before the permanent fault happens. Integration to weather data was implemented in cooperation with the MMEA programme (Measurement, Monitoring and Environmental Assessment). mmea "New services" for business concepts r, r ” r _ _. i . _i_. l' _ , sjIataat-r4:nautrmnrlmrirutrttratt

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