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


Together, we make your blade stronger

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

Together, we make your blade stronger 8 © Bladena 2014 – all rights reserved

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. Together, we make your blade stronger

Confidentiality of proprietary data

Together, we make your blade stronger 11 © Bladena 2014 – all rights reserved

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 USA

Risk for Major repair/Catastropic Incident:

Incidents per year (which could have been prevented) 2

Number of relevant turbines installed (age, operating conditions etc.) 360

Risk for an incident per year 0,56%

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 damage 300.000$

Loss of revenue ( 10 weeks, 0,5 mw per hour, $0,05/kw) 42.000$

Total cost per incident 604.500$

Annual Failure Risk Allocation per turbine 3.358$

Unplanned Repair allocation

Inspection 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/inspection 31.750$

Risk for requirement for annual repair/inspection 5%

Annual Allocation for Repair and Inspection, per turbine 1.588$

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/inspection 21.550$

Risk for requirement for annual repair/inspection 20%

Annual Allocation for Rapair and Inspection, per turbine 4.310$

Total Annual Allocation 9.256$

Post warranty Operatrion, years (25 years life - 2 years warranty) 23

Total Trailing Edge Cost during post warranty operations 212.884$

Estimated Cost of preventive measure installation (per turbine) 20.000$

Expected operating life 23

Total Savings during expected turbine operation 192.884$

Return of Investment, Years 2,2

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 [email protected] +45 20 145 996 CTO: Find Mølholt Jensen [email protected] +45 53 700 276


http://www.bladena.com/

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