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SAMBA DASARIData Science Engagement Leader, Data Science Services,
GE Digital ([email protected])
How Predix Analytics are Driving Exelon’s Digital Transformation
SIYU WUStaff Data Scientist, Data Science Services
GE Digital ([email protected])
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
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 ([email protected])
Samba Dasari
Engagement Leader, Data Science Services
GE Digital ([email protected])