SAMBA DASARIData Science Engagement Leader, Data Science Services,

GE Digital (sambasiva.dasari@ge.com)

How Predix Analytics are Driving Exelon’s Digital Transformation

SIYU WUStaff Data Scientist, Data Science Services

GE Digital (siyu.wu@ge.com)

Exelon Digital Transformation with Predix Analytics

Increase equipment reliability,

increase generation potential,

reduce recovery cost and Predict

outages in the Nuclear, Wind and

Gas Power Generation

Increase assets lifetime by

implementing full Grid Networkconnectivity, understand trendsbased on the historical outages,

predict outages and prepare for

Storms to reduce the damage

Power Generation Transmission & Distribution

Exelon embarked on a Journey with GE for the complete Digital Transformation using GE Predix

and Analytics

Power Generation

Predix Analytics Use Cases

Power Generation Pilots Summary

Performance Indicators

“Lighthouse”

Equipment Reliability“Watch Tower”

Equipment ExcellenceAPM

Operational Excellence

Wind Generation Forecasting

Opportunity/Problem

Site performance issues and

cost of recovery

Lost MWhrs due to equipment

reliability and transition to

condition based maintenance

Lack of efficiency and transparency

of equipment data

Efficiency disparity between

two blocks at Colorado Bend

Revenue loss due to forecast

inaccuracies

Outcomes Pilot: Nuclear fleetFleet declines

Potential: 50% decrease in

recovery costs = $2MM

Productivity

Contractor, consulting,

recovery, OT costs

Pilot: ByronLost MWhrs due to

equipment reliability

Potential: 10% decrease in lost

Nuclear MWhrs = $5MM+

Revenue

Penalties

Replacement power/cost

Pilot: Colorado BendAsset visibility & reliability

Potential: 1% increase in fleet

generation = $18MM

Revenue

Energy efficiency

Maintenance costs/time

Pilot: Colorado BendFuel savings/

Generation potential

worth $400k

Potential: 1% increase in fleet

generation = $18MM

Revenue

Capacity, production

Pilot: Wind Fleet Forecast accuracy

Generation potential;

Up to 3% AEP for pilot

sites

Potential: 1-2% accuracy =

3% AEP

Revenue

Growth in markets

Pilot Duration 6 months 6 months 6 months 5 months 5 months

Gas WindNuclear

Nuclear Power Generation – Predictive Model

Predix Analytics Use Cases

Transmission & Distribution

Transmission & Distribution Analytics Cases

Asset HealthAPM

Network Connectivity Analytics

Storm Readiness Analytics

Outage Prediction Analytics

Historical Outage Analysis

Opportunity/Problem

Geo-Spatial asset risk score

based on probability of failure

and severity/ consequence of

failure that integrates asset

condition data from intelligent

devices and other data sources

with static data sets to move

from time/ failure based

maintenance and replacement

strategies to condition/ risk based maintenance and replacement strategies.

Leverage increased precision in

remote monitoring

capabilities (meter voltage, power

quality, DA device

data, etc.), and combine with

constraints identified

through GIS analytics and CIS/

CIMS to expose and eventually automatically correct inaccuracies in the electrical connectivity model.

Utilize localized weather models,

historic outage data, and asset

records to more accurately

forecast storm impact on the grid,prepare appropriate system

configurations, storm hardening

and other storm readiness

measures. During a storm, improve accuracy of restoration projections by using real-time damage assessment data from the

field.

Develop a model to predict near-term outages based on

machine learning of system

data (e.g. voltage, sag / swell

data from AMI, relay data from

smart substations, recloser

momentaries), historical

outage information,

vegetation management, asset records, asset condition, etc. to increase grid

reliability.

Utilize geo-spatial, visual and

statistical analysis to identify reliability risks previously hidden within historical outage information,reliability metrics and asset

records, to make optimal

investments in reliability

improvement programs.

Outcomes• Predictive Asset

maintenance to avoid cost

• Effective Grid maintenance

by keeping the asset health

in good condition

• Reliability improvement through

greater accuracy in identifying

outages, resulting in fewer false

outages reported)

• Enablement of future use cases

through improved accuracy of

network model

• Backbone for all the other T&D

use cases

• % decrease in customer outage

duration, resulting in Avoided

Customer Interruption Minutes

(ACIM)

• Increase in “Power, Quality, and

Reliability” category

• Improved customer satisfaction

creating economic value

• % decrease in customer

outage duration, resulting

in Avoided Customer

Interruption Minutes (ACIM)

• Increase in “Power, Quality,

and Reliability” category

• Improved customer

satisfaction creating

economic value

• % increase in

optimization

• % increase in productivity

in producing historical

outage analysis

• % decrease in customer

outage frequency,

resulting in Avoided

Customer Interruptions

(ACI)

Asset Performance Optimization

Manage T&D asset fleet as a whole

Quantify impacts of DER adoption

Customers report 15% reduction in O&M expense – with increased asset life

Continuous improvement of maintenance strategy

Network Connectivity Analytics

Transformer

Switch

Recloser

Undergroun

d Feeder

Overhead

Feeder

Fuse

Outage Prediction Representative POC Sample

Historical Outage Analysis

Storm Readiness - Machine Learning Approach

Historical Outage Weather

– ~85,000 outages

– 2014 to 2015

– 16 attributes

• outage, storm_id

• start, restore_datetime

• substation, feeder, lockout,

device

• Isolate device lat, long

• cause

• Customers interrupted,

minutes

Shapefile

– Actual past weather

– Retrieved by individual outage

coordinates (~3M rec)

– 14 days prior to and 3 days after

individual outages

– Hourly readings

– 14 attributes

• temp, apparent temp

• humidity, dew point

• precipitation intensity, prob., accu.

• cloud cover

• wind bearing, gust, speed

• ozone, uv, visibility

– 4 levels

• Grid, township, section, quarter section

– Tested on “township”

Storm Readiness POC

20 November

2017

Offering: GE Digital Data Science Accelerator

` `

PREPARATION INDUSTRIAL DATA SCIENCE WORKOUT

EXECUTIVEBRIEFING

TECHNOLOGYBRIEFING

OUTCOMEQUALIFICATION

DAY 1 DAY 2

DATAEXPLORATION

4 WEEKS 2 WEEKS

MVPDEPLOYMENT

ANALYTIC MODELING

6 WEEKS

Problem Identification +

Business Outcome

exploration

KPI Identification;

Use Cases Prioritization

and Refinement

Data Type + Sources

Identification

Finalizing the Use Case

based on data availability

MVP (minimum viable

product) identification

GE Fastworks GE Data Science Agile Project Management, Physical/Empirical/Digital

GE Digital Data Science Services: Offerings

2

Data ScienceExploration

2

OutcomesExploration

workout

1

Data ScienceSolution

3

Data Sciencetraining

4

Time

2 days

2 days

7 days

7 days

Time

5 days

29 days

31 days

31 days

Time

12 weeks

12 weeks

custom

custom

Time

2 days

7 days

custom

custom

Offering’s Level

Starter

Standard

Premier

Enterprise

Reach out to GE Digital Data Science Services team to learn the difference between Starter, Standard, Premier, and Enterprise levels

DSS_Engagements@ge.com

DSS_Engagements@ge.com

Enable Value: GE Digital Data Science Services

IndustrialAnalytics

EmpiricalValue

+ =

§ Asset Performance Management

§ Field Service

§ Predix Connect/Studio

§ Custom built Data Science analytics

§ Platform capabilities

§ Way to manage multiple

value-tied apps

§ Develop& Deploy fast

GE Data Science Professional & Advisory Services: Implementation, Managed, Customer Success

Take The Next Step

To learn more about this topic, please take the following next steps…

1. Action one: visit Predix booth at Tech Hall and talk to Data Science team

2. Action two: schedule a free executive briefing on GE Data Science Services

3. Action three: choose the most relevant Data Science Service Offering

Thank You

Siyu Wu

Staff Data Scientist, Data Science Services

GE Digital (siyu.wu@ge.com)

Samba Dasari

Engagement Leader, Data Science Services

GE Digital (sambasiva.dasari@ge.com)