Edge to Enterprise IoT Analytics for Electric Utilities and Manufacturing

Transcription

1 Edge to Enterprise IoT Analytics for Electric Utilities and Manufacturing Powered by, SAS Edge to Enterprise Analytics Platform April 10, 2018 Krishna Mayuram, Lead Architect (Big Data & Han Yang, Senior Product

2 Electric Utility Challenges Customer Impression Paying increasing month bills Power is Out Utility Challenge Can equipment be repaired BEFORE they fail? Potentially 38% improvement in reliability Monitor power from solar and wind to ensure grid stability Reduce cost for customer Improve public safety

3 Analytics Lifecycle with AI/ML/DL Train, Predict and Act ETL Training Analytics Data Storage f Deploy Alerts / Reports/ Decisioning Deploy IoT & Sensor Data Intelligent Filter / Transform Inference Model Execution Inference

4 Electric Utility AI : Sensor Data Flow Offline Advanced Analytics and Exploration (how to prevent it?) Training (Data Center) An event can be normal or represents an equipment falere. Event Identification Event Qualification (how bad was it?) (what happened?) What is the magnitude of equipment failure events? Streaming data analysis: the system generates n measurements/sec Transform Inference (Edge) Power Plant Primary Substation Event Detection Data Quality / (did something happen?) Data: power frequency, voltage, current, phasor angle, High-voltage transmission line Secondary Substation Analytical techniques can be used to detect deviation from the normal. 3. At Data Center 2. In Edge & Data Center 1. In Edge Main goal: detect and understand events that are affecting the power grid, with the objective of keeping the grid stable. Develop analytics to: Detect events on the network Categorize the event on the network Direct appropriate action based on the event Capture data for post event analysis

5 Edge to Enterprise IOT Analytics Platform: Electric Utility AI Visualization of streaming data using SAS ESP Streamviewer Data Mgmt. & Pattern Detection using ESP Visualization of streaming data using SAS ESP Streamviewer C Update Streaming models KAFKA Pattern Detection using ESP Pattern discovery using SAS Visual Analytics and SAS Visual Statistics ISA Data: power frequency, voltage, current, phasor angle, CGR1K End-to-End Network Mgmt. ( IOT FN. Director) Application Life Cycle Mgmt ( FogDirector) Firepowe r Firepower Connect to external apps with ESP connectors Inference (Edge) Training (Data Center)

6 AI, Analytics, and Machine Learning in Smart Manufacturing

7 Edge Architecture With Kinetic

8 Edge to Enterprise IOT Analytics Platform: Manufacturing AI Visualization of streaming data using SAS ESP Streamviewer Data Mgmt. & Pattern Detection using ESP Visualization of streaming data using SAS ESP Streamviewer C Update Streaming models KAFKA Pattern Detection using ESP Pattern discovery using SAS Visual Analytics and SAS Visual Statistics ISA Data: power frequency, voltage, current, phasor angle, Application Lifecycle Managements ( Fog Director) End-to-End Network Manamgnet ( IOT FN Director) Analytics Lifecycle Managements (AI/ML/DL SAS ESP) Inference (Edge) Firepowe r Firepower Connect to external apps with ESP connectors Training (Data Center)

9 Demo

10 Resources Edge-to-Enterprise IoT Analytics for Electric Utilities Solution Brief and SAS Edge-to-Enterprise IoT Analytics Validated Design CVDs/_SAS_Edge_to_Enterprise_IoT_Analytics_Platform.html

11 Thank You!