Post on 13-Aug-2019
transcript
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. Logics behind this sketch of O&M-future
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.
IEA Task33 Workshop Berlin, 23.9.2015 WTG&Park Comtroller for O&M Support page 6
1. Logics behind this sketch of O&M-future
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
IEA Task33 Workshop Berlin, 23.9.2015 WTG&Park Comtroller for O&M Support page 7
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
IEA Task33 Workshop Berlin, 23.9.2015 WTG&Park Comtroller for O&M Support page 8
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
IEA Task33 Workshop Berlin, 23.9.2015 WTG&Park Comtroller for O&M Support page 9
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
IEA Task33 Workshop Berlin, 23.9.2015 WTG&Park Comtroller for O&M Support page 10
++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
IEA Task33 Workshop Berlin, 23.9.2015 WTG&Park Comtroller for O&M Support page 11
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
IEA Task33 Workshop Berlin, 23.9.2015 WTG&Park Comtroller for O&M Support page 12
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!
IEA Task33 Workshop Berlin, 23.9.2015 WTG&Park Comtroller for O&M Support page 13
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
IEA Task33 Workshop Berlin, 23.9.2015 WTG&Park Comtroller for O&M Support page 14
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
IEA Task33 Workshop Berlin, 23.9.2015 WTG&Park Comtroller for O&M Support page 15
6.4 Math. models for KPIs
Research institutes
Power plant operators
Time for questions ….
… or comments