Iberia Monitoring System:Data Analytics- a new approach to asset management.

Rui Manuel Vilhena EDPP

Pablo J. Alvarez Vigil EDPEs

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Agenda

Agenda

1. EDP´s Generation in Portugal and Spain Overview

2. Asset Management and the challenge of the Data

Leap

3. Previous steps and lessons learned at EDP

4. Future developments at EDP

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EDP´s Generation Fleet: Iberia PRO

TechnologyNº Units

#Net Capacity

MWNet Production

GWh

Coal 7 2.424 13.232

CCGT 9 3.808 5.242

Hydro 145 6.115 16.141

Nuclear 1 155,5 (15,5%) 1.239

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How data impacts on traditional asset management policies

Evolving Business Environment

Power Plants are being dispatched in MIBEL• New operating regimes (cycling + secondary reserve)

• Paradigm Shift: Efficiency, Flexibility and Reliabilitybecomes the new key value drivers

Strategy

Focus on operational performance

• Prioritize power plants in terms of their relevance to the business and operating regimes

• Within the power plants, define critical assets and systems

according to RCM/RBM

New Trends

Big Data + Analytics

Source: (100 Data and AnalyticsPredictions Through 2020, Gartner)

As we are collecting more information regarding the operation of our assets,

What role these new trends could have on our strategic

priorities?

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How data impacts on traditional asset management policies

“Big Data in general is defined as high volume, velocity and variety information assets that demand cost-

effective, innovative forms of information processing for enhanced insight and decision making” (Gartner)

The 5 V’s of Big Data:

“Big Data is Right-Time Business Insight and Decision MakingAt Extreme Scale” (J. Higginbotham)

Velocity

(Sense of opportunity

of the analysis)

Variety

(Different data

sources and types)

Veracity

(Impacts the reliability

of the analysis)

Volume

(Large datasets to

analyze)

Value

Source: (Big Data Analytics, Oxenti)

How data impacts on traditional asset management policies

Data Sources

The Big Data cycle:

Process

Insight

Action

Collect

Combine, Aggregate and Convert Data

(Creating Datasets)

Taking Actions

Analytics

Adapted from: (Big Data Basics, J. Higginbotham)

“Analytics is defined as the scientific process of transforming data into

insight for making better decisions.” (informs)

“Analytics is the discovery, interpretation, and communication of meaningful patterns in data” (Davenport & Harris)

How data impacts on traditional asset management policies

But … there are different types of Analytics with distinctive potential business value and difficulty:

Adapted from: (Gartner)

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Former Steps: From data silos to integrated information

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Former Steps: Deployment of RBM and RCM strategies on critical assets

Designing Maintenance Strategy based on RCM-RBM

Assessment of equipment

Definition of Maintenance strategy

On-line monitoring

Field audit and diagnosis

Historical records

Equipment impact on system

reliability / risk

Assessment of equipment condition

Assessment of equipment criticality

Maintenance strategy based on “trade-off” risk vs.

reliability analysis

▪ To define

maintenance

actions per

equipment type

▪ To determine

real cond. and

criticality of

each equip.

▪ To determine

maintenance

needs based

on previous

assessment

Objective

Off-line monitoring

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Former Steps: Learning about predictive capabilities (in-house development)

Hydro: Monitor Hill Chart efficiency Transversal: Access the health index of the power

transformers according to a criticality asset score

Access and monitor performance losses due to water intake obstructions

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Former Steps: Learning about predictive capabilities (in-house development)

Thermal: Evaluate and monitor heat exchange in the

components of a pulverized coal boiler

Monitor the pressure difference in the SCR reactor during the catalyst layers useful life

Loss of operational margins on the boiler induced draft fans

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Former Steps: Learning about predictive capabilities (marketwise solutions)

People (Key Pilar)

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Towards and integrated MDC

Goals &

Key Value Drivers

Processes Tools

What a MDC is not:• its purpose is not to remotely operate the power plants,

• neither to access the performance of the O&M personnel,

• it is not built to hierarchy top up the power plants,

• It is not an emergency response team,

• and it is definitely not a decision center.

Goals: Develop insights from monitoring the health

and efficiency of the assets in a predictive manner andturn them into value.

Key Value Drivers:

• Avoid efficiency losses

• Increase the availability (reducing unscheduled

downtime by anticipating failures)

• Reduce maintenance costs (early warnings prior

to failures allows for a better resource allocation)

MDC (Monitoring & Diagnostic

Center)

Many tools and monitoring platforms are available marketwise …

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MDC Challenges: Data Context and Structure

“Data Scientists, according to interviews and expertestimates, spend 50% to 80% of their time mired inthe mundane labor of collecting and preparingunruly digital data, before it can be explored …”(Steve Lohr, The New York Times)

Source: (Harvard Data Science Course)

It is crucial to improve the context of our raw data:

• label the source tags according to a transversal and coherent structure across the entire fleet

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MDC Challenges: Technology

New challenges: IT/OT convergence + IIoTTraditional Architecture

New challenges: Cloud computing & cybersecurity

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MDC Challenges: Organizational

Workflow integration of MDC with O&M

New Digital Profiles for new developments

New Organizational Functions for MDC exploration

The challenge of the Culture Transformation:Impact on the whole Organization

References

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die, E. Siegel

100 Data and Analytics Predictions Through 2020, Gartner

Big Data Basics: An Introduction to Big Data and How It Is Changing Business, J. Higginbotham

Big Data Analytics, OxenTI Solutions, M. C. Purificação

Predictive Analytics using R, J. Strickland

Competing on Analytics: The New Science of Winning, T. H. Davenport, J. G. Harris

Eight Levels of Analytics for Competitive Advantage

Business Analytics & Digital Business

Perspectives: Turning Big Data into Valuable Insights, Hydro Review

The New York Times (Steve Lohr)