White Paper RAM

SAFER, SMARTER, GREENER

AUTHOR: Victor Borges, RAM Product Manager, DNV GL - Software

DATE: 31 May 2015

WHITEPAPER

MAROS LITE Energy from air

SAFEGUARDING

LIFE,

PROPERTY

AND THE ENVIRONMENT

Date: 31 May 2015

Prepared by DNV GL - Software

Copyright DNV GL AS 2014. All rights reserved. No use of the material is allowed without the prior written consent of DNV GL AS.

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TABLE OF CONTENTS

TABLE OF CONTENTS .................................................................................................................... I

1 INTRODUCTION .............................................................................................................. 1

2 IMPORTANCE OF WIND TURBINES .................................................................................... 2

3 MAINTENANCE SUPPORT ................................................................................................. 2

3.1 Crew 2

3.2 Workboats 2

3.3 Helicopter support 2

3.4 Spare parts 3

3.5 Safety equipment 3

4 LIFECYCLE COST ANALYSIS (LCC) .................................................................................... 3

4.1 Annual Discount Rate 3

4.2 Capital Expenditure 4

4.3 Operating Expenditure 4

4.4 Product Price 4

5 RAM ANALYSIS ............................................................................................................... 4

6 CASE STUDY .................................................................................................................. 4

6.1 Introduction 4

6.2 Reliability Block Diagrams and Reliability data 6

6.3 Maintenance resources and priority 7

6.4 Financial aspects 10

7 RESULTS ..................................................................................................................... 11

8 SENSITIVITY ANALYSIS ................................................................................................. 15

8.1 Planned renewal 15

8.2 Conditional monitoring 19

9 CONCLUSION ............................................................................................................... 21

10 ABOUT THE AUTHOR ..................................................................................................... 21

11 REFERENCES ................................................................................................................ 22

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1 INTRODUCTION

A fantastic source of energy surrounds the Earth: our atmosphere. Our atmosphere is formed of air

which is a mixture of different gases, liquid and solid particles. Heat energy from the sun warms up the

atmosphere and asymmetrically the Earth.

Warm air is lighter than cold air; cold air is more dense than warm air so it sinks down through warm air.

On the other hand, warm air rises through the atmosphere. When the air rises through the atmosphere,

it creates a low pressure area, when it sinks through the atmosphere, it creates a high pressure areas.

In order to balance this different pressure, air particles move from areas of high pressure (cold air) to

areas of low pressure (warm air). This movement of air is known as the wind.

The wind is influenced by number of factors such as the earths movement and its irregular surface. For

instance, where warm land and cool sea meet, the difference in temperature creates thermal effects,

which causes local sea breezes.

A wind turbine is a machine that transforms the kinetic energy of the wind into mechanical and then

electrical energy. Wind turbines consist of a foundation, a tower, a nacelle and a rotor. The foundation

prevents the turbine from falling over. The tower holds up the rotor and a nacelle (or box).

The nacelle contains large primary components such as the main axle, gearbox, generator, transformer

and control system. The rotor is made of the blades and the hub, which holds them in position as they

turn. Most commercial wind turbines have three rotor blades. The length of the blades can be more than

60 metres.

Figure 1: How a wind turbine comes together (IRENA, 2012)


2 IMPORTANCE OF WIND TURBINES

In March 2007, European Union leaders set the 2020 targets, committing to address the continuously

increasing energy production from hydrocarbon sources. The main goal is to become a highly energy-

efficient and low carbon economy.

In this programme, they mention the 2020, 20-20-20 targets. The programme describes an integrated

approach to climate and energy policy that aims to combat climate change. One of the 20s refers to

increasing the share of EU energy consumption produced from renewable resources to 20%.

To achieve this target, wind turbines must play an essential role. Good news, one might say - a solution

to the global issue. The bad news however, is how do we ensure continuous performance from a big

fan sitting in remote locations and out at sea? We can do this in the same way that we already support

big metal structures out at sea, albeit with some additional challenges!

3 MAINTENANCE SUPPORT

Akin to an oil and gas production platform, the operation is 24/7. The wind turbines are unmanned,

imposing challenges to maintenance campaigns. Space is also limited and only small spare parts can be

stored in the turbine.

Maintenance strategy is one of many topics that have to be explored in detail for construction and

operation projects. The maintenance strategy is supported by a number of resources, some of which will

now be discussed.

3.1 Crew

The maintenance crew is formed of technicians, and sometimes specialised personnel are required to

perform specific maintenance tasks. One of the biggest challenges is getting maintenance crew to and

then on and off the offshore turbines and substations to carry out work. There are two major factors that

influence the approach taken to gaining access:

Travel time the time needed to shuttle a service crew from the crew base to the place of work.

A number of constraints must be taken into account such as limited shift hours available, the

mobilisation time taken to prepare the crew as well as the travel time to transport crews to

different locations in the wind farm. The goal should be to optimise the utilisation of the crew.

Accessibility after getting on the wind turbine, there is a window of time where the turbine can

be safely accessed. This will obviously depend on the transportation means and the sea and

weather conditions. For example, significant wave height will prevent a vessel to transfer crew

and equipment.

3.2 Workboats

Workboats are an essential part of any offshore wind maintenance strategy. These boats are essential

for logistical services by transporting technicians and equipment from the shore to the wind farm. The

services are extended depending on how far offshore the sites are. For example, distant offshore sites

may also use workboats to ferry technicians between the offshore base and turbines.

3.3 Helicopter support

Helicopters transport technicians to and from the wind farm. They are particularly important when

environmental conditions, such as sea state conditions, make the wind turbine inaccessible. The


helicopter size will depend on the extension of the wind turbines and its distance from the supporting

location as well as the service being delivered. Helicopters are mainly used for crew transportation but

can also be used to transport spare parts.

3.4 Spare parts

Spare parts are typically required to repair shutdowns caused by unscheduled failures or even on an

opportunistic basis. Opportunistic maintenance is commonly practised on a variety of systems, the

objective being to offset unscheduled repairs by planning an inspection, overhaul or renewal of specific

equipment at regular intervals, with the aim of improving long term productivity (reliability) of the

system. As with opportune maintenance there is an optimum strategy to yield maximum cost efficiency.

3.5 Safety equipment

Inspections of safety/performance critical equipment items are essential to maintain safety requirements

and performance standards.

Obviously, inspections must be carried out by qualified personnel and be supported by a number of

safety maintenance resources. Inspection frequency is typically every six-months or annually, depending

on the equipment being addressed.

4 LIFECYCLE COST ANALYSIS (LCC)

Different analyses are used to explore a number of variables that will impact directly not only the uptime

of the system but also operational expenditure and cash flows. Lifecycle cost analysis (LCC) - which is

typically used to evaluate the financial performance of different projects - seem to be even more

important to wind farms when compared to the oil and gas industry.

Wind turbines are a one thousand year old technology, however they have seen a rapid evolution in

design in recent times as their application to utility scale power production has brough investment. An

important aspect of the rapidly changing design and usage philosophies around the technologies is to

consider the potential financial performance. This is fundamental to ensure return on investment and

understand the risk and uncertainties in a venture.

A tool commonly used to the compare the financial aspects of different projects is the Net Present Value

(NPV). Net present value takes account of cash flows from the project and allows us to compare future

projections to their present values by applying a discount factor. After taking this factor into account,

projects become directly comparable. Should the value of the capital inflows exceed those of the

outflows after the selected discount rate has been applied then the project will provide a positive cash

flow, and the greater the value the better. However, if the NPV is negative the returns from the project

are less than the outflows and attempts should be made to minimise the NPV.

To be able to produce an NPV figure, the following information is needed.

4.1 Annual Discount Rate

An annual discount rate in percentage form must be ascertained. This will relate the worth, in financial

terms, of a future sum to its present value. For example, assuming a discount rate of 10%, this implies

that 100 returned 1 year from now is worth 90.90 (the calculation is 100/[1+10%]) in today's terms.


4.2 Capital Expenditure

Information regarding the capital expenditure of the project can be used. This describes the initial capital

expense outlayed at the beginning of the project and any other expenses incurred during the lifetime of

the project.

4.3 Operating Expenditure

Operating expenditure information is required for manpower, spares and extras. This consists of the cost

in day rates of the resources used, as well as any mobilisation/de-mobilisation costs incurred from their

use. Extra costs can be added if required. The output represents the cost of operating and maintaining

an installation over its lifetime.

4.4 Product Price

Other information required prior to producing the NPV figure is the product price. This is the stipulated

initial price per unit, in the given currency (e.g. 0.16/kWh), and any changes which can be expected to

occur to this price through the life of the system.

5 RAM ANALYSIS

Reliability, Availability and Maintainability (RAM) analysis is a methodology used to predict asset

performance based on reliability and maintainability. As with many other branches of modern

engineering, system performance analysis is probabilistic as opposed to deterministic in nature.

This methodology is well established and used in many domains such as the oil and gas and transport

industries. For wind power applications the RAM methodology has a remarkably similar approach. When

applying RAM analysis to renewable energy there are a number of slight modifications that must be

accounted for such as the wind speed profile and the probability of the wind blowing in different

directions. Applying RAM analysis to renewable energy is an excellent opportunity which leverages

decades of human investment into RAM simulation methodologies and applies it to a progressive,

sustainable industry.

6 CASE STUDY

6.1 Introduction

For this case study, a 3.5 MW wind turbine is considered to be operational from 2015 to 20401. Wind

turbines contain more than 8,000 components, many of which are made from different materials

including steel, cast iron, and concrete. From these 8,000 components, 20 items have been selected as

production critical. These items are listed below.

1 http://www.ewea.org/wind-energy-basics/faq/

How long does a wind turbine work for?

Wind turbines can carry on generating electricity for 20-25 years. Over their lifetime they will be running continuously for as much as 120,000 hours. This compares with the design lifetime of a car engine, which is 4,000 to 6,000 hours.
http://www.ewea.org/wind-energy-basics/faq/


Table 1: List of equipment data

System Equipment item

Bearing Bearing A

Bearing B

Control system Sensors

Controller

Control system Converter

Electrical system Electric control

Gearbox

Shaft

Cooling system

Gearbox

High Speed Shaft coupling

High speed stage bearing

Intermediate speed stage bearing

Generator Generator

Bearing

Rotor

Hydraulics Hydraulic system

Main break Main break

Pitch control Pitch control

Spare break Spare break

These components will be used as the basis for the Reliability Block Diagram (RBD).

One important factor to take into account when predicting the performance of wind turbines is the

capacity factor. Capacity factor is defined as the actual output over a period of time, compared to its

potential output if it were possible for it to operate at full nameplate capacity continuously over the same

period of time. 2

The daily production rate can be calculated as:

(. ) = 24

This capacity factor accounts for periods of low or no wind, transmission line capacity and electricity

demand. Hence, the performance of a wind turbine is not only dependent on its availability but also on

the weather conditions. The average capacity factor for offshore wind turbines is 41%.

The capacity factor used used in our case study is 35% giving a daily production of:

(.

) = 0.35 3500 24

= 29400 . /

2 http://www.nrc.gov/reading-rm/basic-ref/glossary/capacity-factor-net.html
http://www.nrc.gov/reading-rm/basic-ref/glossary/capacity-factor-net.html


Operation and maintenance costs are a large share of the lifecycle cost of offshore wind turbines. The

typical investment3 in operation and maintenance is 13/MWh in 2015. Reducing the cost of the energy

produced by offshore wind projects is a major focus for the offshore wind industry and governments4.

Therefore, understanding the impact of these operational expenditures and how different maintenance

strategies impact the financial feasibility of the wind turbine is vital. Hence, the focus of this case studys

analysis is to model the complex maintenance strategy. This maintenance strategy comprises of multiple

locations and multiple crews and it is described in more details below.

6.2 Reliability Block Diagrams and Reliability data

A Reliability Block Diagram (RBD) is a logical representation of the system connection, taking into

account the path of success of the system mission, in this case power production. If you have items in

series, when one of them is in a failed state there is no way for the system to move forward. However, if

you have items in parallel, it means that there is more than one success path in the system.

The RBD for the wind turbine system is shown in Figure 2:

Figure 2: RBD for the wind turbine system

Each one of the blocks in a reliability block diagram represents one event that can lead to production

loss. In the specific case of this model, each one of the blocks represents an equipment item. Below the

equipment level, the user must define failure modes failure modes are different ways in which the

equipment can fail.

Generic reliability data was added to the model as shown below:

3 http://www.wind-energy-the-facts.org/development-of-the-cost-of-offshore-wind-power-up-to-2015.html

4 The Crown Estate: Cost Reduction Pathways .:. DECC: Offshore Wind Cost Reduction Task Force Report
http://www.wind-energy-the-facts.org/development-of-the-cost-of-offshore-wind-power-up-to-2015.htmlhttps://www.gov.uk/government/uploads/system/uploads/attachment_data/file/66776/5584-offshore-wind-cost-reduction-task-force-report.pdfhttp://www.thecrownestate.co.uk/media/305094/Offshore%20wind%20cost%20reduction%20pathways%20study.pdf


Table 2: Reliability data for the wind turbine system


Failure distribution Parameter 1 Parameter 2 Repair distribution Constant repair time

Bearing Bearing A Weibull (no delay) 9.3 2.5 Constant Repair Time 148

Bearing B Weibull (no delay) 9.3 2.5 Constant Repair Time 148

Control system

Sensors Exponential 6.97 - Constant Repair Time 49.4

Controller Weibull (no delay) 12.48 2 Constant Repair Time 104

Control system

Converter Weibull (no delay) 12.48 2 Constant Repair Time 108

Electrical system

Electric control

Weibull (no delay) 12.5 2 Constant Repair Time 106.6

Gearbox

Shaft Exponential 56.18 - Constant Repair Time 291.4

Cooling system

Weibull (no delay) 13.6 1.1 Constant Repair Time 14

Gearbox Weibull (no delay) 23.415 1.7 Constant Repair Time 336

High Speed Shaft

coupling


High speed stage bearing


Intermediate speed stage

bearing


Generator

Generator Exponential 15.94 - Constant Repair Time 210.7

Bearing Weibull (no delay) 15.3 2.5 Constant Repair Time 148

Rotor Weibull (no delay) 17.32 3 Constant Repair Time 120

Hydraulics Hydraulic system

Exponential 25 - Constant Repair Time 43.2

Main break Main break Exponential 14.55 - Constant Repair Time 125.4

Pitch control Pitch control Exponential 19.23 - Constant Repair Time 95

Spare break Spare break Exponential 20 - Constant Repair Time 78

The reliability data listed above comes from different sources available in the industry. Data points have

been selected to produce more conservative results.

6.3 Maintenance resources and priority

Repair tasks will require a different set of maintenance resources. These maintenance resources can be

grouped to cover a specific range of equipment failures e.g. all pump failures will require one crew and

one spare pump. Obviously, some resources will be shared amongst different equipment items such as

crews and some resources are dedicated to specific set of failures spare pumps for pump failures.

Some of these maintenance resources will have constraints such as number in stock and time to restock

for spare parts. Every time a spare is not available for a job, the time to restock will be of 4 days.


Figure 3: maintenance resources constraints

Further constraints can also be added to accommodate the challenges related to the maintenance

strategy of wind turbines. For example, the accessibility issue can be defined so further to the repair

task it takes around 1-2 hours to access a specific area of the wind turbine.

In addition to information regarding potential constraints, cost data can be assigned to each

maintenance resource. This data consists of the cost in day rates of the resources used, or unit cost for

each spare part as well as any mobilisation/de-mobilisation costs incurred from their use. By summing

up all the cost related to maintenance resources, the operating expenditure can be estimated. This is

described in more detail in the next section.

Figure 4: operational expenditure for the maintenance resources

In order to organise all this information, we have to define Maintenance profiles. Maintenance profiles

are used to group maintenance resources for a specific set of failure events and set repair priority.

For this case study, these profiles will also be used to classify the maintenance tasks required for the

group of equipment items. A list of the different profiles is given below:

Class I Requirements: No Spare part + Access Vessel + 2 Crew member

Class II Requirements: Spare part + Access Vessel (boat) + 2 Crew members.

Class III Requirements: Spare part + Access Vessel (boat) + Helicopter + 6 Crew members.

Class IV Requirements: Spare part + Access Vessel (heavy boat) + Helicopter + 6 Crew

members.


Figure 5: Maintenance profiles defined for failures to the Main bearing

A summary of all the maintenance resources is defined in the following table:

Table 3: Crew constraints

Crew Shift Travel time Daily rate

Technician 08:00 - 16:00 2-4 hours 100/hour

Table 4: Workboat constraints

Number available Mobilisation Mobilisation cost Daily rate

Workboat 1 5 1,500 40,000

Large workboat

1 5 2,000 60,000

Table 5: Helicopter constraints

Number available

Mobilisation Mobilisation cost Daily rate

Helicopter 1 3 2,000 15,000

Table 6: Spare parts:

Spare Name Spare Price ()

Mobilisation cost ()

Lead time (day) Classes for type of maintenance

Rotor 1,200,000 24,000 15 Class B

Gearbox 700,000 14,000 10 Class D

Generator 200,000 4,000 4 Class B

Brake 70,000 1,400 2 Class B

Main Bearings 120,000 10,000 1 Class D

Bearing 50,000 1,000 2 Class C

Shaft 100,000 3,000 8 Class D


6.4 Financial aspects

Capital expenditures (CapEx) is normally a known, fixed value. The average capital expenditure for

offshore wind turbines is: 4,500,000.005.

The Operational Expenditure which is normally based on failures and availability of maintenance

resources is variable and hard to estimate up front.

By using dynamic simulation techniques, changes to the asset can be taken into account. This approach

also allows the analyst to account for variations on the value of money over the predicted years

discount rates, interest, etc.

The financial calculation can be extended to incorporate product pricing which enables estimation of the

revenue produced.

The product price defined for this study is 0.16/kWh.

For this case study, the following maintenance cost data is assigned to a number of elements.

Table 7: Maintenance resource cost

Maintenance resources Mobilisation cost

Daily rate

Crew Technician 1,000 2,400

Vessel Workboat 1,500 40,000

Vessel Large Workboat 2,000 60,000

Accessory Helicopter 2,000 15,000

Table 8: Spare part cost

Spare Name Spare Price ()

Mobilisation cost ()

Rotor 1,200,000 24,000

Gearbox 700,000 14,000

Generator 200,000 4,000

Brake 70,000 1,400

Main Bearings 120,000 10,000

Bearing 50,000 1,000

Shaft 100,000 3,000

There are two options when calculating the NPV negative NPV or the Standard NPV. The negative NPV

accounts for the potential loss of revenue whereas the standard NPV details the profit.

NPV (Khan, 1993) should have the cash flow discounted back to its present value or the current

estimated product pricing (PP). The cash inflow and cash outflow are summed so the NPV is the

summation of the terms:

5 http://www.scottish-enterprise.com/~/media/SE/Resources/Documents/MNO/Offshore-wind-guide-June-2013.pdf . The capital expenditure of

a typical 5MW offshore wind turbine is 6M. Therefore, a 3.5MW offshore wind turbine is around 4.5M
http://en.wikipedia.org/wiki/Discountedhttp://www.scottish-enterprise.com/~/media/SE/Resources/Documents/MNO/Offshore-wind-guide-June-2013.pdf


= ( (

(1 + )) ( )

=1

) ( ) ( (

(1 + )) ( )

=1

)

= ( ) ( (

(1 + )) ( )

=1

) ( (

(1 + )) ( )

=1

)

Where:

t = reference year

i = the discount rate

PP = product price

7 RESULTS

The virtual model for the wind turbine simulates 12500 cycles. This means the model is sampling events

for 500 different lives of 25 years. With this information, the analyst can create a graph that shows how

the Monte Carlo method averages to a stable value after running a number of cycles.

Figure 6: Rolling average a production efficiency from many simulations

The calculated production availability for the system is 96.098% with a standard deviation of 0.331%.

This production availability is averaged from all of the simulated lifecycles. A graph showing the

distribution of the different production availability throughout the different lifecycles can be generated:
http://en.wikipedia.org/wiki/Discount_window


Figure 7: normal distribution

From this graph, the analyst can assess the probability of different levels of production. Traditionally,

engineers are interested on the P10 and P90 probabilities for a system. In this case study, we can see

that 10% of the estimated production availability lifecycle will not exceed 95.675% and 90% of the

estimated production availability lifecycle will not exceed 96.514%.

The criticality graph shows the production loss associated to each event defined at the virtual model.

This is then ranked to show what events are the biggest contributors to the production loss:

Figure 8: criticality at the gas customer node

For this model, the biggest contributors for the production loss are listed below:

- The Planned Maintenance is responsible for 47.027% of the losses

- The Gearbox system is responsible for 21.605% of the losses

- The Generator system is responsible for 15.532% of the losses

The major contributors to losses can be further investigated. For instance, within the Planned

Maintenance share of the pie chart aforementioned, we have different contributors.


Figure 9: contributors to planned maintenance losses

The planned maintenance is expected and the ability to quantify how much production is lost due to

planned shutdowns is really important.

However, production loss can also be tracked for the unscheduled outages such as the example below of

the second biggest contributor, the Gearbox

.

The simulation process keeps track of production levels throughout the simulation process and we can

generate a graph showing the losses and the overall production:


Figure 10: Production level

One interesting trend on this graph is the increasing blue bar, representing the increasing losses, which

should be expected since we have failure distributions that describe an increasing failures rate with time.

A view of the financial aspects of the venture can also be evaluated looking at the cash flow, operational

expenditure and cumulative revenue.

The graph below shows the lost production recovery opportunity. Thus -12.1M shows how much money

we can recover if we can optimise the system to avoid production loss.

Figure 11: Negative Net Present Value

Figure 12: Categorised operating expenditure


8 SENSITIVITY ANALYSIS

For this case study, a few sensitivity cases are going to be explored. Sensitivity cases are changes to the

base case used to investigate potential optimisations in system design, maintenance configuration and

operational strategy.

8.1 Planned renewal

Preventive maintenance is commonly practised on a variety of systems, the objective being to offset

unscheduled repairs by planning an inspection, overhaul or renewal of specific equipment at regular

intervals, with the aim of improving long term productivity of the system.

So, in order to optimise system availability, we will implement planned activities that will renew

equipment items with ageing patterns and, therefore, increasing failure rate. The focus of this sensitivity

case will be to the gearbox system which is the second biggest loss contributor.

Figure 13: overall system criticality

Within this subsystem, we can see that the High and Intermediate speed stage bearing are the most

critical items. So we will target these elements when implementing this maintenance strategy.

Figure 14: Criticality contribution for items below the gearbox system


The reliability data assigned to each element is shown below:


Failure distribution

Characteristic life

Shape factor

Repair distribution

Constant repair time

Gearbox

High speed stage

bearing

Weibull (no delay)

13.5 2.5 Constant Repair Time

312

Intermediate speed stage

bearing

Weibull (no delay)

15.5 2.5 Constant Repair Time

312

The first case will include a Planned Renewal approach only for the High speed stage bearing. Different

time intervals will be tested to assess what is the optimum replacement period of the bearings. The time

frame will be 9, 11 and 13 years.

The production availability of each case is detailed below:

Production efficiency (%) Standard deviation (%)

Base case - No Bearing replacement 96.098 0.331

Bearing replacement 9 year 96.181 0.328



This could be graphed to make the comparison easy to understand.

Figure 15: Average efficiency for the different cases

Production efficiency is marginally improving but a more detailed analysis involving the cost should be

implemented. Every time the bearing is replaced, a new set of spares is used as well as the crew and

different accessories such as the helicopter.

Therefore, we can see a small step in revenue losses when assessing the negative NPV graph.

96.0496.06

96.0896.1

96.1296.14

96.1696.18

96.2

Average efficiency

Base case

9 years

11 years

13 years


Figure 16: Step in revenue loss for the negative NPV

Taking the negative NPV for each scenario, the following table and graph can be produced:

Negative NPV ( Million)

Base case - No Bearing replacement -12.1

Bearing replacement 9 year -12.2



Implementing this maintenance strategy increases the cost by 0.1M. Since the increase in production

availability is marginal, the decision is not to implement this strategy.

The second case to be investigated is replacing both the High speed stage bearing and Intermediate

speed stage bearing.

The production availability of each case is detailed below:

Production efficiency (%) Standard deviation (%)

Base case - No Bearing replacement 96.098 0.331




This could be graphed to make the comparison easy to understand.

Step in revenue loss


Figure 17: Average efficiency for the different cases

When implementing this to both bearing systems the production availability shows an increase of 0.2%

which is an attractive option considering we are looking at a 25 years life span.

The next step is to assess the financial aspects of this new maintenance strategy. The maintenance cost

estimate can be derived by the model by looking into how much resources have been used on average

throughout the 12500 lifecycles.

A graph showing all of the maintenance resource expenditure can be created, as shown below:

Figure 18: Maintenance expenditure for the different cases

There are a few interesting points on this graph:

- The utilisation of some maintenance resources do not change from one case to another. This

makes sense since the new maintenance strategy impacts only a set of variables. For example,

the workboat and the rotor are not impacted by the new strategy.

- The large workboat expenditure shows a decrease compared to the base case this means that

this new strategy effectively addresses failures prior its occurrence which optimises the

resources utilisation. The same applies to the helicopter resource.

95.9

96

96.1

96.2

96.3

96.4

Average efficiency

Base case

9 years

11 years

13 years

0

5000000

10000000

15000000

20000000

25000000

Base case

9 years

11 years

13 years


- The total expenditure is marginally increased by the new strategy when comparing the base case

to the 9 years time interval.

- The expenditure related to the main bearing spare parts increases. However, the replacement is

performed in a controlled environment with all resources available which has a minimum

duration when compared to an unplanned, failure event with complex mobilisation time and

activities.

The negative NPV increases marginally, as shown in the graph below. Therefore there is an increase in

the operating spending which maintain a higher level of availability. Now it is important to understand

what the priority is when operating the wind turbine:

- Better production availability or highest revenue?

It might sound like a simple answer where the suggested maintenance strategy should be dropped and

the equipment items should run to failure. However, not being able to deliver the production defined in

contractual requirements can actually incur in much bigger fines than the investment in the maintenance

strategy. For this reason, the decision is to prioritise production availability and the decision is to keep

this maintenance strategy for the main bearings in the gearbox.

The renewal period will be 9 years as it shows increased production availability with tolerable revenue

loss.

Figure 19: Negative NPV for the 9 years periodic planned renewal

8.2 Conditional monitoring

If a fault is being condition monitored then it is expected that some prior warning will be given of an

impending failure, however, not in all cases. With advance knowledge of an imminent failure, the

ensuing repair task can be dealt with more effectively. Offsetting the downtime that would otherwise

accrue while mobilizing the required maintenance resources. This can minimize losses in system

performance. Also, it may be possible to plan the repair to take place while there is minimum disruption

to the system, e.g. during periods of low demand.

To model this situation, then it is necessary to generate an alert of impending failure, along with a

known failure time or in some instances generating the failure without any incipient warning.

For selected failures that are considered to be in a continuous condition monitoring environment, then

the following parameters are provided to describe the condition monitoring option:

Probability of successful detection;


Incipient failure period.

If an incipient failure is detected then the capacity loss given at failure will prevail for the incipient period

(should be < 100% impact; possibly zero). If the onset of failure is not detected then when the actual

failure occurs its impact will be the capacity loss at repair (most likely 100%).

This approach will not be suitable for situations where the condition monitoring occurs at prescribed

intervals i.e. it must be a continuous process.

After implementing the planned renewal strategy, the new criticality graph shows the Generator as the

second biggest contributor to losses:

Figure 20: Overview of the criticality graph for the 9 years periodic planned renewal

Within the generator, the bearing is the main contributor to losses.

Figure 21: Criticality graph for Generator system

A condition monitoring maintenance strategy is going to be implemented to the bearing in order to

address potential failures. As listed above, the parameters defined for the condition monitoring

maintenance strategy are:

Probability of successful detection = 95%

Incipient failure period = 48 hours

The production availability increases marginally with this new strategy going from 96.327% to 96.391%. This makes this implementation not worthwhile.


9 CONCLUSION

RAM analysis plays a key role when analysing optimal maintenance and operational strategies in the

wind turbines. Informed decisions can be drawn from the model and uncertainty of the production

behaviour can be predicted and therefore avoided or reduced.

By running the base case, the analyst builds an intuitive understanding of how the system may behave

throughout its life.

In addition, a list of possible maintenance strategy options can be created from a base case. Therefore,

the following sensitivities are suggested:

- Implementing a planned renewal strategy for High speed stage bearing. This change is not

financially effective as the increased average efficiency does not cover the increased

maintenance cost for performing planned renewal of the critical part.

- Implementing a planned renewal strategy for High speed stage bearing and Intermediate speed

stage bearing. This new strategy is effective as it shows a good return on investment given the

increase in availability.

- Implementing a condition monitoring strategy for the Bearing in the Generator. This new

strategy shows to not be worthwhile as the increased efficiency is marginal.

This model can be easily extended to incorporate the uncertainty related to the wind profiles, power

curves. New technologies such as Energy Storage Systems (ESSs) can also be incorporated as a buffer

to the stochastic nature of wind. This new area will play an essential role in wind farm applications by

ensuring a higher availability of energy from wind power plants, enabling an increased penetration of

wind power in the energy mix.

10 ABOUT THE AUTHOR

Victor Borges, RAM Software Product Manager at DNV GL, is a chemical engineer with experience

performing risk and reliability analysis for assets in the oil and gas industry. He is responsible for DNV

GLs world-leading simulation software packages Maros and Taro.


11 REFERENCES

Dewan, A. (2014). Logistic & Service Optimization for O&M of Offshore Wind Farms. Retrieved 05 06, 2015, from Delft University of Technology:

http://www.lr.tudelft.nl/fileadmin/Faculteit/LR/Organisatie/Afdelingen_en_Leerstoelen/Afdeling_AEWE/Wind_Energy/Education/Masters_Projects/Finished_Master_projects/doc/Ashish_Dewan_r_UPDATE.pdf

DNV GL, S. u. (2013). Customer Stories Maros and Taro. Retrieved 05 06, 2015, from https://www.dnvgl.com/cases/shell-global-solutions-4051

DNV GL, S. u. (2015). Maros User-Guide. London: DNV GL, Software. DNV GL, Software unit. (2013, March 1). Maros and Taro - prime tools for predicting performance.

Retrieved April 15, 2015, from DNV GL Software: https://www.dnvgl.com/cases/shell-global-solutions-4051

Dowell, J., Zitrou, A., Walls, L., Bedford, T., & Infield, D. (2013). Analysis of Wind and Wave Data to Assess Maintenance Access to Offshore Wind Farms. Retrieved 2015, from https://www.strath.ac.uk: https://www.strath.ac.uk/media/departments/eee/iee/windenergydtc/publications/Dowell2013b.pdf

Estate, T. C. (2013). A Guide to UK Offshore Wind Operations and Maintenance. Retrieved 05 06, 2015, from http://www.scottish-enterprise.com/~/media/SE/Resources/Documents/MNO/Offshore-wind-guide-June-2013.pdf

EWEA, E. W. (2009). The Economics of Wind Energy. EWEA. EWEA, E. W. (2012). Factsheets. Retrieved 05 06, 2015, from

http://www.ewea.org/fileadmin/files/library/publications/statistics/Factsheets.pdf

IRENA, I. R. (2012). Renewable Energy Technologies: Cost Analysis Series. Khan, M. (1993). Theory & Problems in Financial Management. Boston: McGraw Hill Higher Education. Lange, B., Larsen, S. E., Hjstrup, J., & Barthelmie, R. (n.d.). The wind speed profile at offshore wind

farm sites. Retrieved from Research gate: http://www.researchgate.net/profile/Soren_Larsen3/publication/228416435_The_wind_speed_profile_at_offshore_wind_farm_sites/links/0c9605251a01a54f5f000000.pdf

Nuclear Regulatory Commission, U. (n.d.). Capacity factor (net). Retrieved 05 06, 2015, from

http://www.nrc.gov/reading-rm/basic-ref/glossary/capacity-factor-net.html The facts, W. e. (n.d.). Development of the cost of Offshore wind power up to 2015. Retrieved 05 06,

2015, from http://www.wind-energy-the-facts.org/development-of-the-cost-of-offshore-wind-power-up-to-2015.html


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