Innovations with real- time operational data

© Copyr i gh t 2014 O SIs o f t , LLC .

Presented by

Innovations with real-

time operational data:

Drivers & Strategy

Robin Hagemans

Alliander


3

Introduction Alliander

LargestElectricity & Gas Grid Companyin the Netherlands

Facilitate Energy Transition

Asset efficiency

Security in supply energy

3,4 Million

1,6b 7000

E 20,4 min

G 35 sec


Context of our challenge

ChangingBusinessModelsin our

economy

Renewable Energy

Energy Producing Buildings

Energy Exchange

Electric Transport

Storage

toR

ifkin

/Agte

rberg

integration

3th Industrial Revolution


Peaks over the top

to cause more peaks in the energy flows…

growth

HUGE

in connected

RENEWABLES &

sustainable technology

Completely different

use of the grid!

How to deal

with that?


Scenario’s & Strategic options

Usual expansion methods

Grid automation

Tariff differentiation & Smart meters

Smart applications and innovations

Local Storage

6

Steady growth

Business as Usual

+20%

Extreme Transition

+300%


Customer driven transition context

Decentralisation

Always on

Extreme insight

Society trendsinfluence and increase awarenesstowards energy


How could we connect these worlds?

Always on

Decentralisation

ASOCIALCONTEXT

Extreme insight

24/7 insight in the energy grid for any customer and grid operator!

Facilitate Energy Transition

Security in supply energy

data onvAsset efficiency

AGRID


ENERGY =

INFORMATION

By solving this equation!


What’s our cup of tea?

How to get more added value

from operational data?

Business case in assetdata

1 more and other dataservices and applications

2

Demand for increasedecision support capability

3 While the datachain expands

4


Create the right context for Solutions

Transactions GeoSpatial

Realtime

Business

Intelligence

&

Analytics

every

data source

provides one more

context

External

to beat the real (time) data integration challenge


With a middle-out architecture

Asset

& Data

Information

products

External data Geo/Static

Transactional

Service Integration

Standardisation

Knowledge


And start to build:

Problem

Definitions A

Middle-out

Layer

with the first examples to show you…..


Presented by

‘Warm Commissioning’

of the data chain using

PI Datalink

Gies Bouwman

Alliander


Goal of ‘Warm Commissioning’

• Data Chain: from

sensor to end user

15

• Goal: use initial data

to perform basic

plausibility checks

• Commissioning

engineer is thus able

to ‘prove’ that data

chain is consistent

and correct

• Early detected errors

can generally be

resolved at low costs


Background

• Alliander’s expectation for the next 5-7 years

• roll-out of sensor & communication devices

• 5k-15k secondary substations

• On some days, 3 – 5 sites will be equipped

16

• To be done by common workforce of field engineers

• limited IT/OT knowledge & experience

• not the end users of data


To err is human...

Plenty of room for (installation) errors:

• Wrongly selected sensors

• Misplacement of sensors

• Broken sensors

• Phase substitution

• Faulty wiring: cables connected to wrong I/O ports

• Misconfigured RTU, for instance, transformer ratio

• IP address mix-up

• ‘Numeric’ signals wrongly mapped to alphanumeric PI tag names

• Scaling errors translating binary representation to domain values

• Data errors in GIS leading to incorrect tag names

• and we’ve seen many, many more...

17


Challenge: do it first time right!

For each new connected secondary substation, 50+ new tags are added to

the PI database

• I, P, Q, S and φ: per bay and per phase

• U per phase

• LV side, MV side, or both sides

And, in some cases:

• Digital signals and alarms

• Signals related to Harmonics and Power Quality

Challenge break-down

Challenge 1: prove that data from new tags are consistent, both intrinsically

and with existing tags

Challenge 2: do all this while the engineer is still on site

18


Key to solution:

rule-based flow diagram

- Data analytics team: 18 page report, with a decision tree

- Ten simple rules that test whether initial data is plausible,

and if not, what the most likely cause may be

- Example rules:

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PoC: Auto generated

Datalink Workbook

• Data Chain Management tool• PI/AF SDK tool in C#.Net

• Excel Interop to create Excel

• Based on AF: contains the planned I/O

• And has role of CMDB

• See our 2013 EMEA presentation

• Warm Commissioning tool: automatically generated PI Datalink Excel Workbook

• All prepared long before engineer actually installs the sensors, RTU, and modem on site

20

Data Chain

Management

tool

RTU config

SCADA signals

CMDB (AF)

‘Warm

Commissioning’

Workbook

PI tags

http://www.osisoft.com/Templates/item-abstract.aspx?id=10510


PoC: Auto generated

Datalink Workbook

• Decision rules implemented as

Datalink functions like PICurrVal()

and Excel formulas

• Front worksheet shows summary of

all test results

• Subsequent worksheets

• subtests: details of individual tests

• failed subtests are highlighted

• possible reason for failure

• Deployed as Citrix app (thin client)

on iPad

21


<short demo>

Warmopleveren_9 002 531_RTU_10_(Broekseweg - Callisto)_OFFLINE.xlsx


Future work

23

• Simple, mobile app that directs the user to the

cause of installation errors

• Perhaps looking like this:


Robin Hagemans

• [email protected]

• Team Manager Innovations & Livelab

• Alliander

24

Gies Bouwman

• [email protected]

• Sr. System Engineer/Consultant

• Alliander

mailto:[email protected]

mailto:[email protected]


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