2014 Wind Turbine Blade Workshop- Jensen

Together, we make your blade stronger
1
Bladena
Presenter:
Find Mølholt Jensen, PhD CTO and Founder of Bladena ApS
Bladena provides up-tower retrofitable structural enhancers for blades for wind turbines which once and for all are reducing the cost for maintenance and repairs, increasing the annual production of energy for lowering the cost of energy. Furthermore Bladena own a number of granted patents which all aim after making blades stronger, lighter and more cost efficient.
1 © Bladena 2014 – all rights reserved
Blade and Defects Database
Sandia Wind Turbine Blade Workshop August 2014

-Bladena in Brief
-Motivation
-Guide2Defect database
-A practical example on how to use the data in order to make the most cost optimal Repair and maintenance strategy
-Summary
Outline

Bladena – company background
•Established in 2011
•Spin-out from Technical University of Denmark – RISØ
•7 patents for blade structural enhancement technologies
•Two service partners in North America and one in Europe
•Demonstration projects with strong industry partners
Bladena provides structural enhancement technologies for Wind Turbine blades:
•improve the strength of blades
•reduce the cost of maintenance and repairs
•extend the effective life time of blades
•maximize the annual production of energy

- More and more blade damages are observed in the field.
- The WTOs have focus on minimising maintenance costs.
-The WTOs need better statistical data to improve the maintenance strategy.
- Repair and maintenance cost will increase when blades get longer and are installed offshore.
- A general accept that the maintenance strategies can be optimized e.g. by prevent damages before occurrence.
- Also improved inspection strategy is considered e.g. look inside the blade.
Motivation
Blade failures

Status public avaliable data
Failure frequency, ISET 2006 Gcube insurance, 2012 (# events, not cost)
Minimize Cost of Energy → Maximize Life Time → Maximize Reliability
A comprehensive work has been published by researchers and others, but usually qualified inputs are lacking. Especially the failure rate is very often based on a too small number of blades.

Motivation – What is the most cost optimal maintenance strategy?
Operations and maintenance:
•Corrective (wait to fail)
•Conditioned (monitor condition)
•Preventive (time set) Regular inspections to decide which action is needed and to avoid expensive repairs
•No damage: do nothing or install preventive measure
•Minor damage: repair and install preventive measure
•Major damage: exchange blade
•Preventive maintenance: install in similar blades

-On-line categorization tool which categorize blade failures.
-The Guide2Defect gives a statistical overview of defects.
-The G2D-database is intended to be a tool to WTOs so maintenance strategies can be optimized e.g. what to inspect on the specific blades and also what is the most cost optimal inspection interval etc..
-G2D-database will provide more qualified input for cost modelling models, which will enable owners and service providers to make more accurate decisions. The database need following input:
•Environmental impact, wind class and site classification
•Manufacture turbine and blade
•Position of the defect
•Defect description e.g. crack length and direction
Guide2Defect database

Important. Please note there is absolutely no interpretation
Defect tree – Input to Guide2Defect database

Example of an input screen dump for Guide2Defect

Site
WTG
Blade
Defect
Proprietary data for WTO owner (location, performance etc.)
“Generic” Bladena Statistics for Product innovation etc.
Bladena has ensured that all confidential data received from Dong Energy, E.On and Vattenfall will not be shared with 3rd party. To ensure this the confidential data have been blurry/anonymised.
Example: A wind turbine park consist of 70 2MW turbines from a certain OEM owned by Wind turbine owner A. The site is placed at a site B in Denmark.
All information about the damages, blade type etc. will be available but not the specific site name (B) and the name of the WTO (A). Also the specific location will be blurred and in this case North Europe will be the region.
Confidentiality of proprietary data

WTO owner
Dashboard
Bladena G2D Input
Bladena Generic data. Internal use, product innovation
Data
Data
Data
Data
Data
Data
Data
Data
Data
Statistics
-In order to get a better statistical representation Bladena wants to enter agreement with other WTOs.
-The database will show how statistical reliable the post processed data is. If it is really poor a dark red light will indicate that conclusion based on this data set is not reliable
Sharing data with industry

The WTOs will be able to utilise data from Guide2Defect in several ways, e.g.
1.Failure reports.
2.Catalogue of all defects and where on the blade the defects are located.
3.A report to WTOs on how the growth of the damages have developed from the start and until today.
Possible statistic reports from Guide2Defect

Post processing data and Cost simulations
Bladena is parrallel with that all the data is uploaded preparing cost models so maintenance strategies can be improved. Bladena is collaborating with leading experts e.g. Prof. John D. Sørensen from Aalborg University of Denmark in order to support WTO to make a cost optimal maintenance strategy.
G2D- Database
Processing
Cost modelling

Expected defect development
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0
5
10
15
20
25
30
Turbine age
Defect per year - %
0
1
2
3
4
5
6
7
8
9
0
5
10
15
20
25
30
Defect per year - %
Turbine age
Thesis to be confirmed through data
Note: Numbers only for example

Anonymous WTO has observed TE-failures. Together with Bladena a specific blade type has been analysed. The findings were:
-Two Blades with TE-failures have been too costly to repair and the blades have been replaced the last year.
-Smaller failures in the TE-area or at aft shear web is noted to be on approx. 25% of the blades
Example on how to use data to make the most cost optimal decision

Cost simulation
Based on experience
Conclusion: Retrofit of a preventive solution will have a Return on Investment at 2.2 years if installed
Failure Risk Allocation (crane required) - an example from USARisk for Major repair/Catastropic Incident: Incidents per year (which could have been prevented)2Number of relevant turbines installed (age, operating conditions etc.)360Risk for an incident per year0,56% Cost per Incident: Crane for down and up lift of blade(s)200.000$ Investigation and adminstrative cost (operator, 500 hrs, $125/hrs)62.500$ Repair cost including shipping , blade(s) and collateral damage300.000$ Loss of revenue ( 10 weeks, 0,5 mw per hour, $0,05/kw)42.000$ Total cost per incident604.500$ Annual Failure Risk Allocation per turbine3.358$ Unplanned Repair allocationInspection and repair cost, one action only (per turbine, 4 men, 5 days, $100/hrs)24.000$ Invistigation and adminstrative cost (operator, 50 hrs, $125/hrs)6.250$ Loss of revenue ( 5 days, 12 hrs per day, 0,5 mw per hour, $0,05/kw)1.500$ Total cost per repair/inspection31.750$ Risk for requirement for annual repair/inspection5% Annual Allocation for Repair and Inspection, per turbine1.588$ Planned Repair and Inspection Allocation (Combined) Inspection and repair cost, share for trailing edge (per turbine, 4 men, 3 days, $100/hrs)14.400$ Invistigation and adminstrative cost (operator, 50 hrs, $125/hrs)6.250$ Loss of revenue ( 3 days, 12 hrs per day, 0,5 mw per hour, $0,05/kw)900$ Total cost per repair/inspection21.550$ Risk for requirement for annual repair/inspection20% Annual Allocation for Rapair and Inspection, per turbine4.310$ Total Annual Allocation9.256$ Post warranty Operatrion, years (25 years life - 2 years warranty)23Total Trailing Edge Cost during post warranty operations212.884$ Estimated Cost of preventive measure installation (per turbine)20.000$ Expected operating life23Total Savings during expected turbine operation192.884$ Return of Investment, Years2,2

Description
Bladena has expertise in Root Cause Analysis (RCA) and have performed this several times. The RCA-analysis usually contains following:
1.“Description” of the failure including the occurrence based on experiences seen on other blades. In the future data from G2D will be used.
2.Depending of the Failure type a RCA analysis is performed including available measurement if any and numerical simulation (FEM) etc.
3.Description of the Root Cause.
4.Suggestion to how to solve or mitigate the problem.
5.Recommendation to solution(s). Recommendation takes cost and risk into consideration.
Example of a RCA/Failure report

- There seems to be an increasing failure rate on larger blades and there is a need for optimizing the maintenance strategies.
- The maintenance cost are expected to increase when blades get larger and the fleets get older.
- There is a need for better documentation of defects.
- The Guide2Defect will hopefully give the WTOs information for statistically based decisions for cost optimizing the maintenance strategy.
Summary

Thank you for your attention Any Questions?
Bladena
www.bladena.com
CEO: Ryan Lauridsen
ryl@bladena.com +45 20 145 996
CTO: Find Mølholt Jensen fmj@bladena.com +45 53 700 276

2014 Wind Turbine Blade Workshop- Jensen

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2014 Wind Turbine Blade Workshop- Jensen