Artificial Intelligence and EPRI: What’s Next AI Power Lab Simmins - Grid... · AI Power Lab AI...

© 2017 Electric Power Research Institute, Inc. All rights reserved.

Dr. Andrew PhillipsVice President,

Transmission and DistributionDr. John Simmins

Technical Executive

The Applications and Challenges of AI Technology in Power System Forum

April 27, 2018

Artificial Intelligence and EPRI: What’s Next

AI Power Lab

3© 2018 Electric Power Research Institute, Inc. All rights reserved.

EPRI.AI/EPRI AI Energy Hub/ EPRI AI Power Lab


AI Power Lab AI effort led by Dr. Andrew Phillips. Facilitate AI collaboration among the power

industry, AI providers, and AI researchers. Develop effective solutions ultimately benefiting

utility customers. The lab will serve as an innovation hub that

brings together established companies, startups and universities.

Research, advance, and accelerate AI applications across electricity generation, delivery and use.

Dr. Andrew PhillipsVice PresidentTransmission and Distribution Infrastructure AreaPower Delivery & Utilization


AI – Converting Data to InsightsTY

PE O

F CL

IENT

STRUCTURED UNSTRUCTURED

APP

THIN

G

Image

Voice/Text

Workflow/Process/Web

Sensor Network

AI Platforms – Democratization of AI

We'll look back in 10 years and see this time as the inflection point of when computer architectures became neural.”

–Naveen Rao, Intel

Big Data

ArtificialIntelligence

Deep

Lea

rnin

g

Neural Network

Fuzzy Logic

Clustering

Data Analytics

Big Data

Deep Learning

Neural Network

Data Analytics

Big Data

Neural Network Fuzzy Logic

Clustering

Machine LearningDeep Learning

Clustering

Big Data

MachineLearning

Deep Learning

Neural Network

Fuzzy Logic

Clustering Data Analytics

How does machine become neural and mimic a human brain?

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1/0

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Examples of handwritten digits from U.S. postal envelopes.

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What are the Hidden Layers Doing?

Feature Extraction



Feature Extraction – Feature Vertical Lines

vertical lines


Feature Extraction – Feature Horizontal Lines

horizontal lines

Deep Learning Automates Feature Extraction (Machine is Learning on its own)

small circles

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1/0

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The Final Output Layer Comes out with the Answer

Number Nine 9

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Number 9


A Person on a beach flying a kite

Sandwich Cut in Half


Traffic Light Hanging from a Wire I think it’s a Large Room


EPRI Power Vision API


Condenser Tube Calcium ScalingDeteriorated Wooden Pole Top


TestingData

Validating Data

TrainingData


Data Privacy/Security/Governance

Unmasked Data

Masked PII

Data

Masked CEII Data

Masked Grid Ops

Data

Masked Plant Data

Public Data

Masked Load Data

Level 1&2 Data “Single Factor” Clearance?

Level 3&4 Data “Two Factor” Clearance?

EPRI’s Data Analytics

Board Initiative is Building the

Foundation for EPRI AI Lab

AI PLATFORMS ASSET INSPECTION

CYBERSECURITY

ENVIRONMENTAL

GRID OPERATIONS/DER INTEGRATION

IoT / IIoT

TRANSPORTATION

RESIDENTIAL/ COMMERCIAL BUILDING ANALYTICS

OTHER

PREDICTIVE ANALYTICS

Vision of EPRI.AI/EPRI AI Lab/EPRI AI Energy Hub– Advancing AI for the Electricity Sector

Utility & EPRI Experts Innovators/Startups/UniversitiesData Sets


Example Use Case – T&D Infrastructure


Opportunities for Distribution Assets

Asset Inspections

Vegetation Management

Storm Assessment

Others?

Acquire

Use

“Street View” Style Images

UAS and Aerial

Crowd-sourced

Images as a Service

AI is the Link


Background Existing GIS information on

distribution assets is often incomplete and inaccurate This is bad if we want to rely

on distribution to implement a smart and resilient electric grid Improving the quality of GIS

data is a time-consuming and expensive exercise, unless……we do this in a virtual

world using virtual utility crews


How would you rate the completeness of your GIS?

Source: Is Your GIS Smart Grid Ready? Esri 2010


How would you rate the completeness of your GIS? Customer –

transformer Customer – phase Topology Street lights Dual use Three phaseOrientation Distributed energy

resources Conflation Type of poles Encroachment


Traditional methods

InspectionsLIDAR


Identifying assets

“Biologically inspired, forward

pass, neural network.

“Using the same visual cues

as a human or animal would

to identify an object.”


3 - Things

Source of data Identify

assetsMeasure

location


Current Steps in AI Based Feasibility Demostration System define street route collect image panoramas at

fixed GPS locations detect vertical structures

using vision system cull false positives to produce

pole hypotheses geolocate producing GPS

coordinates and log results reimage poles to from optimal

location to increase detail precisely locate pole edges

using vision system foveate along extent of pole

to produce asset hypotheses cull false positives quantify properties of assets

and log results


Path definition


Image acquisition


Typical panorama


Neural vision processing module


Vertical structure detection and culling


Geolocation by triangulation


Clustering of multiple image locations


Some statistics for pole detection

TT 83.3% TF 16.7% FT 37.5%


Reimaged higher resolution close up of pole


Precise localization of pole


Foveation to locate transverse assests

1- pole height and width

2- cross member location & projected length

3- street light support strut location and length

4- large cable connection locations


Localization and classification of street lamps


Localization and classification of transformers


Why aren’t we doing it?

“Using image processing, we can detect _____% of our most common defects.”

“Ground based imagery works well for _____, but UAS imagery is better for ______.”

“Automated image processing works, we just have to make sure we _____.”

“Image processing is a possibility, but the technology is ____ years away.”

Fill in the blank:

“We already have access to images of ____% of our assets.”


What we need to succeed

• How to get the right images?

• What are existing data sources?

• Emerging data sources?

• How to get the right images?

• What are existing data sources?

• Emerging data sources?

Acquire

• What technologies are available?

• How much can I trust the results?

• What applications are most appropriate?

• What technologies are available?

• How much can I trust the results?

• What applications are most appropriate?

Analyze

• How do you integrate the results into your workflow?

• How do you integrate the results into your workflow?

Act

Utilities lack the information they need to make a confident decision


EPRI Approach for Distribution Assets Image Analytics

Get Data Facilitate EvaluateObjective

Performance Information


The EPRI Approach

Get Data Facilitate EvaluateObjective

Performance Information

Significant #

Many Scenarios

Various Conditions

Classified

Anonymized

Multiple Technology Providers

“Blind” Data Set

Well defined & implemented approach

Valid Metrics


What next?

Build project team

Acquire imagery

Create datasets

Train & evaluate vendors

Analyze output

Report on results

Next Step:1. Share current practices and any AI projects underway2. Brainstorm potential use cases3. Develop ideas for new image sources4. Executives to identify an SME to contribute5. Connect us with the owners of imagery in your organization


Executive Advisory Panel

Michael LewisSr. VP Distribution

Greg DudkinPresident

Tom KirkpatrickVP Customer &Distribution

Kenny MercadoSVP Electric Operations

Phil HerringtonSr. VP T&D

Matt KetschkeSr. VP Customer Energy Solutions

Marco BruzzanoVP Distribution Operations

Dave KarafaVP Distribution

Cecily BarnesVP Energy Delivery

Cedric GreenVP Support Services

Danny LindseyVP T&D Infrastructure

Jim PrattSr. Director GridModernization

Heidi BenedictSr. Director Business Innovation


Transmission UAV Automation / AI Project Underway

Contaminated Flashed Broken

Automate UAV Flight & Image Capture Paths

Use AI Image Analysis to Evaluate Images

Over 7,000 Images Collected from Members14 Technology Providers Engaged


PARTICIPATING UTILITIES PARTICIPATING TECHNOLOGY PROVIDERS


UAV Images Collected from Participants

25 Categories1. No Condition

2. Flashed Insulators

3. Broken Insulators

4. Missing Cotter Pin

5. …

Transmission Image Database

Training Set 1

Test Set 1

Future Training and Testing

Unusable Images7,876 1,069

550

1,665

3,284

4,592

EPRI


Identified: Cracked Porcelain Disc- 88% confidence

3 Vendor Tests Completed to Date: Examples from Vendor 1

Not Identified: Wood Pole Cavity

Vendor 1 indicated ability to identify 7 of 25 Categories after Training


Performance Quantification: Vendor 1

Weak Negative RelationshipModerate Negative Relationship

Strong Negative Relationship

Very Strong Negative Relationship

Very Strong Positive Relationship

Moderate Positive RelationshipWeak Positive Relationship

Negligible Relationship

Strong Positive Relationship

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Classifier MCC

Wood Pole Cap Defect 0.908

Damaged Damper 0.903

Corroded Connector 0.862

Wood Pole Cavity 0.782

Damaged Conductor 0.774

Broken Porcelain Insulator 0.705

Good Conductor 0

85% of the 7 Condition Categories Identified

Mathews Correlation Coefficient


• Advances in AI create an opportunity for utilities.Why• Increased confidence in the technology and industries’

ability to use it effectivelyWhat• Gather images using existing and emerging techniques• Train and evaluate image processing vendorsHow• Engage utility SMEs and acquire imagesWhat Next


Example Use Case – Customer


Applying Analytics at the Customer LevelUse case – Predicting HVAC Failure Using AMI Data

Objective:Achieve greater accuracy and less latency in predictions of when and where failures are likely to occur.

Approach: Collect industry data Curate data for analysis Run machine learning algorithms to train predictive modelsMake available to innovators to create new algorithms and

applications


Beyond AMI & HVAC… Leveraging Multiple Data Sources and Applications

Advanced Energy

Community


Hold industry workshops

Developing the Framework for a Customer Data Analytics Platform

Develop use cases

Define data requirements

Define architecture and system requirements

Create Customer Data Analytics Platform

Create data lake and analyze

ENERGY MONITORING

Customer Data Analytics SolutionsUTILITY OPERATIONS

& CUSTOMER INTEGRATIONCUSTOMER-CENTEREDENERGY OPTIMIZATION

CUSTOMER ENGAGEMENTDISTRIBUTED DATA RESOURCES


Vision of EPRI.AI/EPRI AI Lab/EPRI AI Energy Hub– Advancing AI for the Electricity Sector

Utility & EPRI Experts Innovators/Startups/UniversitiesData Sets


EPRI.AI/EPRI AI Energy Hub/ EPRI AI Power Lab

Artificial Intelligence and EPRI: What’s Next AI Power Lab Simmins - Grid... · AI Power Lab AI...

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