Component and System Health Monitoring using Turbine and ... · Component and System Health...

Component and System Health Monitoring using

Turbine and Wind Farm Controllers

Dipl.-Ing. Axel Ringhandt bachmann electronic GmbH

Bachmann Group Key Figures

• Headquarter: Feldkirch (Austria) – development and production site

• More than 420 employees – 19 nationalities

• 20 locations in Europe, USA, China and India

• Leading high-tech company for automation

• More than 70.000 installed applications in the wind energy sector

• Main driver: To realise our customers‘ future requirements today.

Industry &

Mechanical engineering

Marine

Onshore Wind

Renewable Energies

Offshore Wind

Bachmann sectors

Bachmann Monitoring References

over 13 years

experience in Wind Energy

Installations in more than 4400 WTG

More than 6 GW in

monitoring

WTG rated power from

650 kW to 6 MW

Onshore and Offshore

installations

WTG portfolio covers 23

OEMs

Experience in 80 different

gearbox types

Know-how from 10

drive train set-ups

More than 450 WFs (2 - 200

WTG)

Presentation Overview

1. Logics behind this sketch of O&M-future

6. Cooperation required to reach objective

IEA Task33 Workshop Berlin, 23.9.2015 WTG&Park Comtroller for O&M Support page 5

2. Basis required for standardized health monitoring

3. Definitions of health indicators

4. Fleet & own-history comparisons

5. Onsite operation & maintenance use cases


1.1 Trends for renewable generation by nature:

• Increase in O&M data complexity per generator

• Increase in fault consequences

• Cost competition against established generation

1.2 Objectives:

• Reduce risk of human overload in data analysis

• Create added value through machine analysis

• Reduce park <–> home server data transfer vol.



1.3 Propositions:

• Not every turbine data has to be brought home to the control room/engineering guys

• WTG controller analysis/tracking of subsystem health saves time to do other important O&M analysis

• Prealarms for abnormal system health degradation improve maintenance efficiency


Note: additional fleet analysis at home by higher math algorithms necessary (not touched here)

2. Basis for standardized health monitoring: 2.1 Unified reference object structure based on function

• simple to understand • suitable for all turbine/farm systems • best: for all renewable energy technologies

2.2 System & component main degradation factors • reference and/or design values • simple system models • one factor or max. a few to get started

2.3 Standard communication protocols • for data- / excerpt- / KPI-transfer into other software


3. Definition of health indicators

3.1 through main wear or degradation events or %-use of main component life factor

on/off cycles, torque changes, temperature profiles, diff pressure, loads, …

3.2 through neural network KPIs

3.3 through class building and Weibull/Gauss distribution factor analysis

3.4 through comparison to ideal physical behaviour


Example: Oil Conditioning System for Main Gearbox MDK51

=MDK51 GP001

=MDK51 GQ001

PIA

=MDK51 BP004

M

PIA

=MDK51 BP005

Main Gearbox

=MDK20

TL001

M

TI

=MDK51 BT003

=MDK51 EQ011=MDK51 HN001

=MDK51 WN001

PIA

=MDK51 BP001

=MDK51 QM001

=MDK51 GP001

=MDK51 BP001

=MDK51 HN001


++MUD10.US102

=MDK51 GQ001

=MDK51 BT003

3. Definition of health indicators

• Mostly „slow“ degradation processes for controller speed level

• Mostly known degradation factors and relationships

• Model/approach may require additional sensors to monitor all degradation relevant factors

• Main critical items only: no degradation -> leave out

• Minimum one indicator or KPI per main system


3. Health indicators: new controller tasks

• Count events, calculates combined values, alarm before design limits are met, alarm if higher equipment use than expected etc.

• Classify analogue values, build Weibull or else parameters, alarm if certain percentage or „loads“ are surpassed

• Report in fixed intervals certain KPIs to park controller

• Park controller runs statistical evaluation program, updates reference values and sends these back to WTGs


4. Fleet and own-history comparisons 4.1 Degradation curves over operating hours or cycles

are mostly typical for the system and can often be obtained from OEMs or industry reliability institutes

4.2 Trend analysis against own history

4.3 Trend analysis against fleet values (park, WinD-Pool)

4.4 In most cases (EU) statistical basis too small to make component behaviour predictions – cooperation is must!


5. Onsite operation & maintenance use cases 5.1 Inspections for public safety (WKP)

Historical health curves check

use in data base driven inspection tools (like REGAS)

5.4 Condition operation after passing health alarms to get maximum / desired life expectancy

5.3 Conditioning for yearly maintenance efforts

5.2 Use leftover time after repair (eg off-shore) to upgrade first x objects with lowest relative health / highest reliability impact


6. Required cooperation to reach objective 6.1 Design lifetime values for equipment

Manufacturers

Component manufacturers Inspectors / Surveyors Operators

6.2 Design structural values Certification body

6.3 Failure curve characteristics

Reliability institutes

component manufacturers


6.4 Math. models for KPIs

Research institutes

Power plant operators

Time for questions ….

… or comments

Component and System Health Monitoring using Turbine and ... · Component and System Health...

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