SAS Real Time Windfarm Analytics Real time decisioning that accelerates time to value with AI/ML
SAS and Cisco solutions designed to collect streaming data from wind turbines and windfarms
Kumar ThangamuthuPrincipal Technical Architect, SAS Institute
Copyright © SAS Inst itute Inc. A l l r ights reserved.
#1AI and Advanced
Analytics Software Platform
IDC 2019
Real Time WindFarm Analytics
• 25 Wind Farms in 5 Continents
• 674 Wind Turbines with geolocation
• ~ 15 Main Kpi’s (Production & Operational)
• 5 different tags with different latency
363,243,806 data records
Real Time Wind Farm AnalyticsUse Case Definition
• Collecting streaming data from wind turbines and windfarms
• Analyze, collect and distribute sensor data in real-time and alert possible issues
• Intuitive visual exploration of near real-time and historical Business KPI’s
• Real-time streaming report
Solution OverviewReal Time Wind Farm Analytics
OSIsoft PI Historian
SQL Server
Analyses at Rest
Production KPI’s
Status Reports
Analyses in Stream
Alarms
Streaming Report
Near Real Time Processing
Real Time Processing
Tags + Reference Data + Business Rules
Tags + Reference Data + Business Rules
Real Time WindFarm AnalyticsKey TakeAways
Real Time Monitoring
Proven and Integrated Solution
Scalable & Flexible & User-Friendly Solution
Cisco Kinetic IoT Platform w/ SAS IoT AnalyticsBringing Distributed Services Architecture (DSA) to Scale IoT Securely
Edge Enterprise Data Center (EDC)
Data: power, frequency, voltage, current, phasor angle,…
Cisco KineticEFM
Cisco 829 ISR
SAS ESP(Edge)
Cisco Kinetic
Powered with Cisco Kinetic DSLink for SAS ESP (Edge) | In alignment to Distributed Services Architecture (DSA), http://iot-dsa.org/
Cisco EDC Infrastructure
SASEvent Stream
Manager
SASESP
Streamviewer
SAS ESP Server
Streaming AnalyticsDevice Management
ESP Model
updates
SAS Visual Data Mining and
Machine Learning
SASVisual Analytics
Cisco EFM Dataflow
Editor
EFM Dataflowupdates
ESP Streams
ESP Model
updates
ESP Versionupdates
EFM Dataflow updates
SAS ESP Studio
SASVisual Statistics
EFM = Edge & Fog Processing Module, for IoT Edge ProcessingESP = Event Stream Processing , for IoT Edge and Cloud Analytics
Enables enterprises to quickly collect, process, and analyze massive amounts of data in real-time, both at the network edge and in the enterprise data center – at scale, distributed, and securely
Key TakeawaysSummary
IoT isn’t about the data … it’s about the value it enables!
OUTCOMES
Streaming/At-Rest, Advanced Analytics
MULTI-PHASE ANALYTICS
Edge-to-Cloud, Performance, Scalability
DIFFERENTIATED TECHNOLOGY
Full lifecycle support … Governance, Accountability,
Management
ENTERPRISE PLATFORM
Growing investment, Expanding ecosystem, New
channels
OPPORTUNITY