Condition Monitoring Benefit for Onshore Wind ...

Condition Monitoring Benefit for Onshore Wind Turbines: Sensitivity to Operational Parameters David McMillan & Graham Ault Institute for Energy & Environment, University of Strathclyde [email protected], [email protected]

Abstract The economic case for condition monitoring applied to wind turbines is currently not well quantified and the factors involved are not fully understood. In order to make more informed decisions regarding whether deployment of condition monitoring for wind turbines is economically justified, a refined set of probabilistic models capturing the processes involved are presented. Sensitivity of the model outputs with respect to variables of interest are investigated within the bounds of published data and expert opinion. The results show that the levels of benefit are dependent on a variety of factors including wind profile, typical downtime duration and wind turbine sub-component replacement cost.

1

Introduction

Wind power is currently regarded by many policy makers and utilities as playing an important role in delivering desired targets on carbon emission reductions and diversity of supply. For this reason an extensive programme of planning and construction of wind farms is underway, with over 10GW wind capacity currently in the UK planning system alone [1]. If all these projects are completed, then wind will comprise 24GW of the total UK installed capacity [2], or around 25% of the future total generation mix. Additionally, the recent UK energy white paper [3] has confirmed continuing government support for the wind industry, in the form of the renewables obligation, until at least 2027. If these trends continue, future utilities will have generation portfolios comprising a substantial proportion of wind power. This recent rapid construction of wind farm capacity has also resulted in widespread installation of condition monitoring (CM) systems for wind turbine generators (WTs). These systems provide information to the wind farm operator, with the goal of improving operational efficiency via more informed decisionmaking. As the number of operational wind farms increases, more focus will be placed on effective operations and maintenance (O&M), which may include use of CM. Wind farm operators, keen to manage their plant as economically as possible will also be eager to apply the most cost-effective maintenance policies available. Therefore, any prospective maintenance policy based on condition information must have clear financial benefits: otherwise a more conventional maintenance scheme is likely to be adopted. This paper contains a set of methods to quantify the value of these maintenance policies, particularly condition-based maintenance (CBM). One key feature of the proposed approach is the idea that the deterioration and failure characteristics of a WT can be captured via use of a Markov Chain. Such a model can be embedded within a probabilistic operation and maintenance simulation reflecting constraints of the operating environment such as impact of maintenance policy. Due to the resultant complexity of such a system, Monte Carlo simulation (MCS) is utilised to extract the metrics of interest. Comparison of these metrics obtained under different maintenance regimes (periodic, condition-based) enables a quantitative evaluation to be conducted. The set of models developed for this purpose are presented in this paper, and will address two key questions related to WT CM: • •

Are WT CM systems currently cost-effective for onshore conditions? What are the key variables which influence cost-effectiveness of WT CM?

These questions are interesting for several reasons: perhaps the most insightful is that few wind farm operators would be able to give a definitive answer. This paper aims to deliver answers based on a

combination of mathematical models which capture the key operational aspects in order to evaluate the role of CM systems in operation and maintenance of WTs in economic terms.

2

Wind Turbine CM Systems

Most modern WTs are manufactured with some form of integrated CM system: such systems are commonly based on vibration monitoring of the WT drive-train [4] as well as temperature of bearings, machine windings etc. Additionally, several emerging systems are commercially available based on technologies such as lubrication oil particulate content and optical strain measurements [5]. The discussion in [6] provides a particularly insightful review of the state of the art in WT CM systems, while emerging methods include the blade monitoring system presented in [7], the supervisory control and data acquisition (SCADA) interpretation system proposed in [8], and the holistic set of models recently developed in [9].

2.1 Economic Evaluation Published research has shown that via different approaches, the benefit of a condition-based maintenance scheme for onshore wind farms can be quantified. Andrawus et al. [10] compiled detailed costs and extracted sub-component failure rates from 6 years of wind turbine SCADA data, with the goal of deciding a suitable maintenance strategy. A combination of reliability-centred maintenance and asset life-cycle analysis was used, and Monte Carlo Simulation was employed to introduce uncertainty into key variables. This approach suggested that, for the conditions evaluated, a Condition Based Maintenance strategy is the most cost-effective option. Nilsson & Bertling [11] performed a very similar analysis for two wind farms in Sweden and the UK. The main conclusions of the paper are that a decrease in corrective (i.e. breakdown) maintenance is needed in order to justify the CMS: the authors conclude that availability would have to increase by 0.43% annually to cover the CMS costs. Alternatively, 45% of corrective maintenance needs to be ‘displaced’ by preventative maintenance. A contrasting approach by the authors of this paper [12] brought together a set of highly flexible probabilistic models which were populated with data from various sources, including published data and anecdotal evidence from a wind farm operator. Simulations were run in order to compare the performance of two maintenance policies (periodic and condition-based) and thus quantify the benefit of WT CM. The results indicated that the case for onshore condition-based maintenance of WTs appeared to be uncertain. The work contained in this paper builds on the previous work by the authors, refining the models and investigating variables of interest in order to facilitate understanding of the economics of WT CM systems.

2.2 Modelling Techniques A number of different approaches have been adopted for modelling wind turbines and other power systems infrastructure in existing studies. These methods vary depending on the objective of the work: however it is instructive to classify and compare them. Figure 1 shows the three broad categorisations: up/ down model, intermediate state(s), and multiple component intermediate state(s). Reliability studies have tended to use the up/down model: most recently Negra et al. [13] used this as part of a reliability assessment of an offshore wind farm. The intermediate state(s) model, usually characterised as a Markov process, has been used by many researchers in a number of application areas. Anders, Endrenyi and associates [14] [15] first applied the method to power systems equipment, enabling deterioration, maintenance, and knowledge of the plant status to be modelled. Marseguerra et al. [16] and Baratta et al. [17] approach the problem as a discrete event simulation: the Markov Chain deterioration model represents key components in the nuclear safety sector. The key strength of their combined Markov - MCS approach is its flexibility in modelling diverse operating conditions and factors. Billinton and Li developed generation models for conventional plant which captured derated states [18], and showed that the increased detail provided more optimistic solutions than traditional up/ down reliability models. Markov models utilising intermediate states have been applied successfully by a number of authors in asset management applications, with notable contributions in the fields of oil-filled circuit breakers [19], water infrastructure [20], and road networks [21].

Figure 1: Sub-Component Models

The approach adopted by Sayas and Allan [22] involves including all WTs and weather states in a single Markov state-space. The authors do not consider intermediate states, and solve their model analytically, however their work shows it is possible to create a complex multi-state, multi-unit model. Additionally, their approach included use of an adjusted transition probability matrix (TPM) to account for reliability impact of changes in weather conditions: it is observed that this feature could be used to model any change in operating condition. The work in this paper adopts some of the features of this framework: however the model is discrete-time and Monte Carlo simulation is used to find the solution. This combination of methods eliminates the need for parallel simulation which should be avoided due to the chance of introducing undetected simulation correlations causing bias in the result [23].

2.3 Monte Carlo Simulation In order to solve a Markov Chain using analytical techniques, the system in question must be expressed by a set of closed-form equations which can then be solved directly (e.g. Substitution, Laplace transforms etc.) or via numerical iteration (e.g. Newton-Raphson). In the case of more complex systems, this means that some amount of simplification is required: usually in terms of the number of possible states. Since the work in this paper is concerned with every-day micro-management and operational issues of a complex system, the detail of this process is very important, and it is key that few assumptions are made in order to preserve that detail. The model in this paper comprises 28 states with some transitions dependent on external constraints such as wind speed. Such a system is simply too complex to be adequately expressed in closed form, thus ruling out analytical solution. This is the key advantage of MCS: instead of directly finding the mean values from closed-form equations, the behavioural process of the system itself is simulated via use of pseudo-random numbers (PRN), which is why MCS is sometimes described as ‘a series of real experiments’ [24]. By summing and taking the average of these individual samples, an estimate can be made of the true mean. This means that no additional simplification of the system is needed: indeed it is very straightforward to build in constraints which would be cumbersome using analytic methods. The most commonly cited drawbacks of MCS are the generally slow convergence of the solution, and the fact that the result may have significant uncertainty. The first drawback is rendered virtually meaningless by the speed of modern CPUs, while the second can be balanced by observing that an analytic answer without quantification of the uncertainty may result in over-confidence in the result.

3

Asset Simulation Modelling Approach

Three levels of modelling have been developed to quantify WT CM system benefit, as shown in Figure 2. Published data have been used wherever possible to populate the models: in cases where data are not available, expert opinion was sought from industrial research partners. This enables a diverse range of processes to be modelled such as physical deterioration and faults, wind farm yield and weather effects, and high-level asset management decisions: these individual aspects are now discussed.

Figure 2: Multi-level WT Representation

3.1 WT Sub-Component Modelling After the Markov-MCS modelling framework is specified, the next issue is to define which WT subcomponents should be modelled. The sources of information used to determine this were: published subcomponent downtime data, reliability data and wind farm operational experience. 3.1.1

WT Sub-Component Downtime and Reliability Data

Downtime duration information and reliability data for wind turbine sub-components are available, due to the significant number of existing wind farms of various age, type and location in existence across the world. This information represents a useful starting point for understanding and modelling of the wind turbine sub-components. Figure 3 illustrates the downtime contribution from a population of German wind turbines as presented in [25], including both outright failures and wear-related failures. This indicates that the gearbox, generator, rotor and main bearing comprise around 67% of downtime per failure. Sensor Other 3% 6% Hyraulics 4% Electrical System 3% Electrical Controls 3%

Rotor 14% Air Brake 5% Mech Brake 2% Pitch Adjustment 2%

Anemometry 2% Yaw System 3%

Main Shaft/ Bearing 15%

Generator 17%

Gearbox 21%

Figure 3: Onshore Wind Turbine Downtime Distribution Several published studies have examined WT failure rates in detail: at the moment these are probably better understood and documented quantities than downtime duration. The data plotted in Figure 4 shows four components and their annual probability of failure: the studies are those by van Bussel & Zaaijer [26], Braam & Rademakers [27], and two studies by Tavner et al. [28] [29]. The authors used contrasting

methods to yield these figures: for example, [28] used data from the ‘windstats’ database and [26] used expert judgement to quantify the sub-component probabilities. Multiple sources of data are plotted in Figure 4 to illustrate that there is no single accepted figure for sub-component failure probabilities: however there may be a range of values which represent credible approximations. The four component categories are chosen because of their significance either in downtime duration or failure rate, and also because CM systems are available to monitor these components (aside from the electronic parts).

Annual Failure Probability

0.7

van Bussel & Zaaijer MAREC 2001

0.6 0.5

Tavner et al. PEMD 2006

0.4 0.3

van Bussel & Rademakers ECN Report 2004

0.2

Tavner et al. Wind Energy 2007

0.1 0 Gearbox

Generator

Blade/ Rotor

Inverter, Electronics & Control

Figure 4: Comparison of WT Annual Failure Rates 3.1.2

Operational Experience

Dialogue with a UK utility engaged in wind farm O&M yields interesting results and comparison with published studies and data as outlined in the previous section. It is clear through this dialogue that the most significant operational failures are associated with the gearbox and generator components. The reasons for this high significance can be summarised: • • • • •

High capital cost and long lead-time for replacement Difficulty in repairing in-situ Large physical size and weight Position in nacelle at top of tower Lengthy resultant downtime, compounded by adverse weather conditions

The final point can be reinforced when it is understood that typical downtime for a gearbox replacement may be around 600 hours (25 days) according to Ribrant and Bertling [30], while industrial sources suggest a figure of 700 hours (29 days) [31]. Utility operational experience indicates that this figure is highly dependent on availability of a spare gearbox: on one hand a replacement could be carried out in 7 days if a spare were available, whereas long component lead times could result in a delay of up to 60 days in some cases [32]. Taking downtime duration, reliability information and operational experience into account, a 4–component model comprising generator, gearbox, blades and electronic-related failures is selected for the studies presented in this paper. The gearbox and generator were included for operational experience reasons as well as their typical downtime duration. Blades were included as there are emerging methods of monitoring these, and downtime as a result of blade failure has been shown to be significant in Figure 3. Finally, electronic-related component failure is included in order to accurately re-create the overall wind turbine failure rate, since they contribute significantly to this quantity [33].

3.1.3

State Space

The definition of the possible system states has to strike a balance between capturing the processes in adequate detail, while retaining enough simplicity for the model parameters to be quantified. Figure 5 shows gearbox oil temperature data from a WT SCADA system: the component could be categorised into three states: fully up, derated and failed. Similar conclusions can be reached for deterioration characteristics of blades and generators: however electronics failures tend to be instantaneous and require only two states.

Figure 5: CM System Categorisation Taking the above into account, the theoretical number of system states can be calculated as 54 states, since 33 x 2 = 54. The system is reduced to 28 states via use of simplifying assumptions: 1. 2. 3. 4.

Simultaneous component failure events are considered insignificant Simultaneous degradation of components are not considered System must transit through derated state before outright failure All failure states are absorbing states

The first two assumptions are credible given the model time resolution of 1 day. The third represents a significant simplification of the real situation, since a proportion of failures will be almost instantaneous. At the moment it is difficult to quantify what proportion of total failures can be picked up by WT CM, although the model could easily accommodate this feature. The third assumption is therefore held for the rest of the paper, but care should be taken with interpretation of the results since these will provide optimistic modelling of the effectiveness of the CM system. The resultant overall system state space is shown in Figure 6: each large box represents the overall condition of the wind turbine, i.e. the status of the 4 modelled components: up, derated and down. Note that Figure 6 follows the assumptions stated above: for example there is no transition between states 1 and 4 (P1,4=0) since this would break assumption 2, however state 4 can be reached via states 2 and 3, corresponding to assumption 3.

Figure 6: Wind Turbine System State-Space 3.1.4 Transition Probabilities The deterioration behaviour of the system is characterised by a transition probability matrix (TPM), the form of which is illustrated in Figure 7 along with an example of possible parameters. Ideally the TPM parameters would be defined by taking a long-run turbine history and calculating transitions based on this history alone. The main issue with this approach is that a huge amount of data may be required to capture a representative sample of WT faults: therefore other approaches must be considered.

Figure 7: Transition Probability Matrix and Values 3.1.5 Parameter Estimation For simplicity, the single component, intermediate state model in Figure 1 can be considered for the parameter estimation procedure. Since failures are modelled as absorbing states, repair probabilities do not have to be deduced – rather the Markov chain is renewed after downtime, as discussed later. Therefore, two probabilities need to be estimated in order to populate the TPM: • •

Pr(1) Probability of incipient component failure Pr(2) Probability of outright failure when in the deteriorated state

Estimation of Pr(2) was based on anecdotal evidence from industrial sources: an example of this is typical time to failure of a gearbox once incipient failure has occurred [34]. The remaining probability of incipient failure Pr(1) can be deduced by conducting a simple sensitivity analysis procedure: 1. 2. 3. 4. 5. 6.

Set up system simulation: estimate Pr(2) from expert judgement and input to model Read in new incremental guess for unknown parameter Pr(1) Run simulation until steady-state value is reached (# of trials dependent on system complexity) Store result for overall component reliability Repeat steps 2-4 until all input values of Pr(1) are simulated Choose Pr(1) which results in best fit to overall failure rates in Figure 4

It should be emphasised that the data presented here represent a starting point for this type of analysis rather than a definitive set of values: however this is adequate for the purposes of demonstrating for the modelling framework. Future work will be required to better understand and quantify the parameters.

3.2 Turbine Energy Yield 3.2.1 Autoregressive Wind Model The yield model consists of two parts: a power curve model and a wind speed model. The wind model is based on an autoregressive time series characterisation with a single parameter (also known as an AR(1) model), and has the form:

WS − µ = φ (WS t

t −1

)

(1)

− µ +εt

If the wind speed in the previous period, WSt-1, is known, then the next time series value, WSt, can be calculated from the equation above. Classification was achieved through inspection of the autocorrelation and partial autocorrelation functions [35], and the model was fitted using the ordinary least squares fitting technique. A statistical programming language was used to estimate model parameter ø and variance σ2 of the Gaussian noise term εt. This approach enables day to day correlation between wind speed values to be captured within the model. Three representative models have been developed using processed one-hour average SCADA data from a set of onshore WTs: these represent a low, medium and high wind regimes (mean wind speed, µ, for three wind profiles: µlow=6.196m/s, µmedium=6.590m/s, µhigh=6.952m/s). 3.2.2 Power Curve Model Simulated wind speed values can thus be plugged into the relevant power curve, as shown in Figure 8. This is a 2MW rated machine – although any curve could be used. It has characteristic cut in, rated and cut out wind speeds of 4, 14 and 25m/s respectively. 2.2 2

MW Output

1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 0

2

4

6

8

10

12

14

16

18

20

Wind Speed M/S

Figure 8: 2MW Turbine Power Curve

22

24

26

3.2.3 Energy Yield Revenue Calculation In order to calculate the WT revenue, R, three quantities are needed. One is the energy (MWh) generated for the time period in question, which is calculated from the wind model and power curve output. The other two quantities are the market price of electricity, MPElec, and value of renewables obligation certificates, MPROC. The magnitude of these economic variables is set to £36/MWh and £40/MWh respectively. Equation (2) shows the formula used to calculate the revenue stream. Trials

R

=

∑ MWh × [MP T

T =1

Elec

+ MP ROC

]

(2)

These costs are currently fixed in the model, although their variability could easily be modelled deterministically or probabilistically in future studies. Clearly the WT will only produce revenue if it is in a functioning state i.e. up or derated. O&M costs incurred, in addition to component repair and replacement costs, are subtracted from the revenue stream to calculate net income from each WT.

3.3 Wind Turbine Asset Management Models 3.3.1 Maintenance & Replacement Costs The baseline maintenance costs for a 2MW WT were taken as £10K per year. If 6-monthly maintenance is adopted, this corresponds to £5K per maintenance action. Therefore the (planned) maintenance costs of a CBM policy can be calculated as a yearly proportion, depending on the frequency of maintenance actions. In addition to planned maintenance costs, unplanned costs for replacement or repair of major WT components should also be modelled, as they are significant. To capture this, every time the Markov model transits to a failure state, the failed component is identified and repair or replacement cost deducted from the WT revenue stream: two sets of replacement costs for the key WT components are shown in Table 1. One set of figures is based on information used in [12], while the other set of costs is derived using the percentage capital cost method proposed in [36]. Repair cost is assumed to be 10% of the Table 1 values. WT Capital cost is taken as £600,000 per MW installed capacity [37]. Finally, repair of a failed component is considered possible in 50% of cases, an assumption which can be revised in light of data regarding reparability of the WT sub-components. WT Sub-Component Cost for 2MW WT EWEC '07 Cost for 2MW WT NREL '03 Gearbox £100,000 £160,800 Generator £50,000 £80,400 Blade £70,000 £66,400 Electronics Sub £5,000 £5,000 Table 1: Unplanned WT Component Replacement Cost 3.3.2 Downtime Duration Modelling Figure 3 showed that downtime of the gearbox and generator (as well as associated bearings), and rotor blades were highly significant. Defining an exact figure for these quantities is difficult, since there are several factors which have an appreciable impact, including component lead-time, spares availability and the need for appropriately trained personnel. With this in mind, Table 2 illustrates a set of ball-park figures which were arrived at through expert opinion of industrial partners [38]. Component Outage Days Downtime Onshore Gearbox 30 Generator 21 Blade 30 Electronics Sub. 1 Table 2: Typical WT Downtime Duration

3.3.3 Maintenance Weather Constraints Some maintenance and repair actions are subject to weather constraints, set by the owner/ operator for health and safety reasons. Such constraints can represent a problem to the operator, since maintenance activities will obviously have to be inhibited in certain weather conditions such as high winds. Table 3 shows a set of constraints employed by a utility with an extensive wind farm operations portfolio: these are readily accommodated within the maintenance model. Wind Speed (m/s) Restrictions >30 No access to site >20 No climbing turbines >18 No opening roof doors fully >15 No working on roof of nacelle >12 No going into hub >10 No lifting roof of nacelle >7 No blade removal >5 No climbing MET masts Table 3: Maintenance Constraints 3.3.4 Periodic Maintenance Policy The most widely practiced maintenance paradigm in any industry is periodic maintenance [39], and maintenance of wind farms is no different. It is assumed that maintenance actions are 100% successful, and have only a small impact on yield (the duration is assumed to be 1 day), being scheduled during periods of low wind. The actions are weather constrained (see Table 3) and are assumed to be carried out every 6 months. Clearly, in the case of even the most efficient maintenance policies, a certain amount of reactive maintenance is inevitable. While these assumptions represent a further simplification of the true effects of maintenance, there is an issue of how data regarding maintenance effects is obtained. The modelling used in this paper represents an averaging of the individual costs since breaking these down into individual elements requires a substantial amount of data which is not yet available. This is an interesting issue which has been encountered in electricity supply through NAFIRS and similar data collection initiatives, and will be pursued with the wind industry in the future. 3.3.5 Condition-Based Maintenance Policy As previously discussed, one of the chief advantages of the Markov approach is its ability to model condition monitoring knowledge capture. An implicit assumption in this approach is that the CM system can infer the current equipment condition with 100% certainty. Therefore, the model as it stands does not address the issue of possible spurious CM diagnosis: for the analysis presented in this paper, the assumption is held. 3.3.6 Linking Condition and Maintenance Using Risk A key requirement for modelling CBM is the definition of coupling between condition and maintenance actions. In this work the coupling is based on the concept of risk: Krishanasamy et al. [40] summarised use of risk in maintenance scheduling of assets and implemented a risk-based maintenance system for a thermal power plant which prioritised maintenance actions based on risk level. For the particular case of Markov chain representation of a WT system used in these studies, the risk in each state can be expressed:

Risk (state ) = ∑ Pr(event ) × Im(event ) N

(3) N

Pr(event) is the probability of transition to a failure state and Im(event) is the impact of that particular component failure should it occur, which could comprise a number of economic terms, but is currently simply the component replacement cost. Using equation (3), each operational state (fully up or derated) can be classified according to three relative risk levels: low, medium and high. Once calculated, the magnitude of risk for each state can be used as an indicator to determine how urgently repair work should be scheduled by the operator of the WT. Table 4 shows the wait time in days for each risk interval: these time values were determined by conducting simple sensitivity studies. As with periodic policies, when the wait

time has elapsed, maintenance will only be carried out if weather conditions are favourable. Using the equipment state and wait times, the 6-monthly periodic maintenance policy is replaced with a condition based policy. States Risk Level Wait Time Days 4,8 HIGH 5 2,3,6,7 MED 18 5 LOW 200 Table 4: Wait Times Coupling Condition & Maintenance The maintenance policies have been presented, along with the representations of the WT and associated modelling, enabling the questions posed in the introduction to be answered based on quantitative results.

4

Case Studies

A number of case study simulations are presented to illustrate the capabilities of the methodology described in previous sections. The models were implemented in Fortran and solved within five minutes in all cases. In order to obtain statistically sound results, each simulation consists of a 14,000 trial simulation run 30 times: 420,000 trials in total. When 14,000 trials were run, this almost always resulted in the turbine residing in each of the possible failure modes at least once. In reality a WT may only experience a sub-set of the failures possible (since conditions and equipment vary from site to site): the spread and frequency of this sub-set of failures may be a contributing factor to the perceived effectiveness of the maintenance policy. A simple statistical calculation can be carried out in order to establish confidence limits (L) of the simulation results:

L

=±

Z ×s N

(4)

Where s is the standard deviation of the samples, and N is the number of samples taken. If the degree of confidence in the result is set to 95%, then the Z score is equal to 2.045 (using student-t distribution with 29 degrees of freedom). Using this statistical tool it is possible to assert that the real mean value is 95% certain to lie within the bounds of the upper and lower confidence limits [41].

4.1 Model Metric Benchmarking A short study was conducted to confirm that the models produce output metrics with a sufficient level of accuracy as compared with real or assumed ball-park figures. Table 5 shows the output of a WT simulation subject to periodic maintenance. The mean wind speed µmedium is equal to 6.590m/s – in this case equivalent to a capacity factor of roughly 26%. ‘NREL 03’ component costs from Table 1 are adopted for this study.

Failure Rates

Annual Metric Average s Upper Lim Lower Lim Availability % 96.71 0.68 96.96 96.46 Yield MWh 4395 34 4408 4382 Revenue £ 246193 16383 252310 240076 Overall Turbine 1.073 0.130 1.122 1.025 Gearbox 0.249 0.085 0.281 0.218 Generator 0.187 0.072 0.213 0.160 Electronics 0.379 0.104 0.418 0.340 Blade 0.258 0.053 0.278 0.239 Table 5: Model Metrics for Periodic 6-Monthly Maintenance The first observation is that the annual sub-component failure rates in Table 5 all fall within the bounds of credible values introduced earlier in the paper in Figure 4, and therefore represent acceptable figures. A figure of 98% is often quoted for wind turbine availability, which compares fairly well with the 96.71%

mean value produced by the model. The annual yield in MWh can be estimated by using a simple calculation: (5) = × Rating × ×

MWh

YEAR

Hrs

YEAR

MW

CF A

%

Assuming a capacity factor (CF) of 26% and availability (A%) of 98%:

MWh

YEAR

= 8760 × 2 × 0.26 × 0.98 =

4464MWh

This can be considered adequately close to the simulated mean value of 4395MWh in Table 5. Based on this yield, the annual revenue can be calculated:

R

YEAR

= MWh YEAR × (MP ROC + MP ELEC ) − C O & M

Annual O&M cost (CO&M) is taken as £10,000 per turbine. MPElec and MPROC are defined in 3.2.3, giving:

R

YEAR

= 4464 ×

(40 + 36 ) − 10,000 = £329,264

The disparity between this value and the simulated £246,193 can be attributed to real CO&M being very much larger than the assumed £10,000 in the above calculation. Indeed repair, and especially replacement, of major components can have a large impact on the revenue stream, and this feature is captured in the models. In certain individual years the turbine may generate high levels of revenue: however such instances may be offset by years where a major unplanned outage occurs. In conclusion, it can be seen that the presented model simulations provide realistic outputs. With this established base case, an evaluation of condition monitoring can be conducted.

4.2 Condition Monitoring Evaluation In this section a comparison is made between a 6-monthly periodic maintenance policy and a condition based policy, with the goal of answering the questions posed in the introduction. The simulation conditions were the same as for the benchmark study (µmedium equal to 6.590m/s, ‘NREL 03’ component costs). Table 6 summarises the average values taken from the 30 simulations.

Failure Rates

Annual Metric PERIODIC CBM Availability % 96.71 97.72 Yield MWh 4395 4434 Revenue £ 246193 270072 Maintenance Freq. 2.000 2.208 Overall Turbine 1.07 0.74 Gearbox 0.25 0.06 Generator 0.19 0.05 Electronics 0.38 0.37 Blade 0.26 0.25 Lost Energy MWh 124.72 78.65 Table 6: Annual Average Values for Simulation Output Metrics One of the central points of interest is how the revenue streams of the two approaches compare. Figure 9 shows a mean annual value of just over £23,000 per annum for condition-based maintenance, relative to the widely-used periodic maintenance policy. The confidence limits indicate a minimum annual benefit of around £15,000. Assuming a post-warranty useful life of 15 years, this represents total operational savings of £225,000 per turbine.

280

Revenue £K

270 260 250 240 230 220 PERIODIC1

CBM

Figure 9: Comparison of Annual WT Revenues Including Confidence Level Figure 9 represents the economic case for CM systems, for the conditions evaluated. One aspect, which has not received much attention to date, is a good understanding of how different operational factors can influence the cost-effectiveness of WT CM systems.

4.3 Sensitivity to Operational Parameters Several parameters of interest were investigated to gain insight regarding sensitivity of CBM cost effectiveness. The parameters are: 1. 2. 3. 4.3.1

Sub-component replacement cost Wind regime Magnitude of downtime, specifically for gearbox replacement Sub-Component Replacement Cost

Table 1 illustrated two sets of figures for WT sub-component replacement costs. The results in previous sections have used only one set of figures, defined as ‘NREL 03’ [36]. In this section, these results are compared to simulations using ‘EWEC 07’ values [12] and are displayed in Figure 10.

Annual CBM Benefit £

37000 32000 27000 22000 17000 12000 7000 2000 -3000

EWEC 071 NREL 03

Figure 10: Impact of Component Cost on CM Economic Effectiveness The results show that these costs certainly have a significant impact on CBM cost-effectiveness. If the lower values are used (EWEC 07), the result is similar to that derived in [12]: it is roughly the same magnitude, and the confidence limits show that the benefit is uncertain. Use of the NREL 03 values results in much larger, more certain benefit levels: perhaps this is best explained by the ~60% increase in cost

magnitude of generator and gearbox for the NREL 03 cost scenario (see Table 1). Although this may seem like a very large cost increase, supply chain issues in the wind industry have recently pushed component costs upwards [42]. This may only be a short-term issue: however it may also be an issue which affects the numerous wind farms in operation today, and is therefore of interest. 4.3.2

Wind Regime and Downtime Duration

Two other quantities of interest are the wind regime and downtime duration, and their influence on CM cost-effectiveness. It is expected that both will have some influence on the ‘lost energy’ component of CM economic performance. As discussed previously, three wind profiles have been modelled: µlow=6.196m/s, µmedium=6.590m/s, µhigh=6.952m/s. Their corresponding capacity factors are 21%, 26% and 30%. Additionally it has been mentioned previously that downtime duration is highly dependent on availability of spares, especially in the case of the gearbox. Therefore the modelled gearbox downtime will be set to the figures suggested in section 3.1.2 i.e. 7, 30 or 60 days. Figure 11 shows the annual average CBM benefit as a function of the variables mentioned. 25000 20000

ANNUAL CM BENEFIT £

15000 10000 5000 0 60 DAYS GEARBOX DOWNTIME

0.30 30 DAYS 7 DAYS

0.26

CAPACITY FACTOR

0.22

Figure 11: Annual CBM Benefit This result shows some suggestion of coupling between the quantities of interest, for example the increasing benefit at 22% CF as the gearbox downtime is increased. The rest of the result seems inconclusive, or suggests little coupling at all: the 60 days gearbox downtime seems to result in decreased CM benefit with increasing CF magnitude, which is highly counter-intuitive as the expectation in this scenario would be that the lost energy would become a significant factor and thus CM benefit would increase. Some light is shed on this by drilling down into the result, and looking at the percentage of CM benefit which is attributable to lost energy as a function of the same variables: this is shown in Figure 12.

35% 30% 25% 20%

% CM BENEFIT SAVED ENERGY

15% 10% 5%

60 DAYS

0%

30 DAYS GEARBOX DOWNTIME

0.30

7 DAYS

0.26 0.22

CAPACITY FACTOR

Figure 12: % of CBM Benefit Attributable to Lost Energy

This result indicates that these parameters do indeed have an appreciable contribution to CM benefit, and that they do so in a manner which would be intuitively expected: i.e. as lost revenue per downtime event increases, the greater the economic success of the CM system through avoidance of lengthy unplanned outages. This result has interesting implications for offshore wind turbines, since increased downtimes and greater capacity factors (>40%) may be expected in the offshore environment, along with larger turbine ratings. This seems to suggest that saved energy will be an even bigger contributing factor to CM benefit than illustrated in Figure 12. The counter-intuitive nature of Figure 11 may therefore be explained by two different hypotheses: 1.

The avoidance of catastrophic component failure is the dominant contributor to the economic case for WT CM deployment, rather than wind regime, downtime duration or other factors related to ‘lost energy’.

2.

The models as they stand do not take adequate account of incurred condition-based repair costs. If the CM system detects a serious incipient fault, repair of that fault may not be trivial, despite increased planning scope provided by the CM system.

Although the models have presented a largely clear case for condition-based maintenance of onshore wind turbines, future work should be geared towards taking account of these modelling inadequacies.

5

Conclusions

An improved set of models to quantify the benefits of condition monitoring systems for onshore wind turbines have been presented in this paper. The results indicate that the economic benefit of onshore WT CM is clear in the majority of cases evaluated, although more modest component replacement costs result in an unclear economic case. Since higher component replacement costs make outage consequences more severe, it can be concluded that this is a key variable and important enabler for WT CM cost-effectiveness: however it should also be noted that the incurred costs of periodic and condition-based maintenance may need to be modelled in more detail to substantiate this conclusion. Figure 12 showed that as capacity factor and unplanned outage downtime duration increase, the percentage of CM benefit attributable to energy capture increases significantly. This may be significant for future large offshore wind farms comprising units rated in the 3-5MW bracket. For the onshore cases evaluated in this paper, Figure 11 seems to indicate that other factors such as failures experienced by the WT over the operational lifetime are more dominant. Future work will focus on model improvement through inclusion of factors such as capital and ancillary costs of the monitoring, possibility of erroneous CM diagnosis (false positives and false negatives), explicit modelling of transducer reliability, CM vs. wind turbine design redundancy, inclusion of more detailed SCADA data and consideration of market based ‘non-delivery’ of electricity penalties as a result of unplanned WT outages. The analysis in this paper has been presented from the viewpoint of the owner/operator: however it would be very interesting to consider how the framework could fit into a wider analysis of the overall power system, considering issues of backup generation and energy interruption costs. This could be particularly interesting in the case of extreme wind conditions causing a wind farm to shut down, moving from almost full output to zero output as well as increased probability of failure of mechanical components [22]. Finally, it would be interesting to compare the operational merits of emerging WT technologies such as direct drive with more established designs: such an analysis could establish which technology is the more cost-effective solution from an O&M viewpoint, which may be particularly important for proposed offshore wind farms in deep water.

6

Acknowledgements

The authors wish to gratefully acknowledge Yusuf Patel, Peter Diver and Matt Smith, of Scottish Power, ITI Energy and Macom Technologies respectively, for their assistance in compiling the information contained in this paper. The authors would also like to thank two anonymous reviewers for their insightful

comments. This research was conducted under the PROSEN project (www.prosen.org.uk), EPSRC grant number EP/C014790/1.

7

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