Krishna Mayuram, Lead Architect (Big Data & IOT) @krismayuram

Han Yang, Senior Product Manager @hanyang1234

April 10, 2018

Powered by Cisco, SAS Edge to Enterprise Analytics Platform

Edge to Enterprise IoT Analytics for Electric Utilities and Manufacturing

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

Electric Utility Challenges

https://www.weforum.org/agenda/2016/06/what-does-the-internet-of-things-mean-for-the-energy-sector/

Deploy

Analytics Lifecycle with AI/ML/DLTrain, Predict and Act

ETL

Analytics Data Storage

Alerts / Reports/ Decisioning

Dep

loy

f

IoT & Sensor Data Intelligent Filter / Transform

InferenceModel Execution

Training

Inference

Data Quality /

Transform

Event Detection(did something

happen?)

Event Identification(what happened?)

Event Qualification(how bad was it?)

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

Data: power frequency, voltage, current, phasor angle, …

Power Plant

Primary Substation

SecondarySubstation

High-voltage transmission line

Streaming data analysis:the system generates n measurements/sec

Analytical techniques can be used to detect

deviation from the normal.

An event can be normal or represents an equipment

falere.

What is the magnitude of equipment failure events?

Main goal: detect and understand events that are affecting the power grid, with the objective of keeping the grid stable.

Offline Advanced Analytics and Exploration(how to prevent it?)

1. In Stream @ Edge

2. In Stream @ Edge & Data Center

3. At rest @ Data Center

Electric Utility AI : Sensor Data Flow

Inference (Edge)

Training (Data Center)

Data Mgmt. & Pattern Detection using ESP

Data: power frequency,

voltage, current, phasor

angle, …

Cisco UCS

Pattern Detection using ESP

Visualization of streaming data using SAS ESP Streamviewer

Application Life

Cycle Mgmt

(Cisco

FogDirector)

Update

Streaming

models

Connect to

external apps

with ESP

connectors

Edge to Enterprise IOT Analytics Platform: Electric Utility AI

Cisco UCS

Cisco UCS

Cisco UCS

Pattern discovery using SAS Visual Analytics and SAS Visual

Statistics

Cisco UCS C

Cisco

ISACisco

Firepowe

r

KAFKA

Cisco

Firepower

Inference (Edge) Training (Data Center)

Visualization of streaming data using

SAS ESP Streamviewer

Cisco CGR1K

End-to-End

Network

Mgmt.

(Cisco IOT

FN. Director)

Smart ManufacturingAI, Analytics, and Machine Learning in

Edge Architecture With Cisco Kinetic

Data Mgmt. & Pattern Detection using ESP

Data: power frequency,

voltage, current, phasor

angle, …

Cisco UCS

Pattern Detection using ESP

Visualization of streaming data using SAS ESP Streamviewer

Update

Streaming

models

Connect to

external apps

with ESP

connectors

Edge to Enterprise IOT Analytics Platform: Manufacturing AI

Cisco UCS

Cisco UCS

Pattern discovery using SAS Visual Analytics and SAS Visual

Statistics

Cisco UCS C

Cisco

ISACisco

Firepowe

r

KAFKA

Cisco

Firepower

Inference (Edge) Training (Data Center)

Visualization of streaming data using

SAS ESP Streamviewer

Application Lifecycle Managements

(Cisco Fog Director)

End-to-End Network Manamgnet

(Cisco IOT FN Director)

Analytics Lifecycle Managements

(AI/ML/DL – SAS ESP)

Demo

ResourcesCisco Edge-to-Enterprise IoT Analytics for Electric Utilities Solution Briefhttps://www.cisco.com/c/en/us/solutions/collateral/data-center-virtualization/big-data/solution-overview-c22-740248.html

Cisco and SAS Edge-to-Enterprise IoT Analytics Cisco Validated Designhttps://www.cisco.com/c/en/us/td/docs/unified_computing/ucs/UCS_CVDs/Cisco_SAS_Edge_to_Enterprise_IoT_Analytics_Platform.html

Thank You !