# Decision Support Models for Operations and Maintenance for Offshore Wind Farms: A Review
## 2 Operation and Maintenance: Influential Factors
### Degradation and Failure Modeling  
The occurrence of a failure in a wind turbine in a wind farm gives rise to a necessary repair and forms the cause of any maintenance action. Since the occurrence of a failure directly causes maintenance actions and is therefore the reason for the connected costs, the existing analyses are concerned with the probability and frequency of failure occurrence. The main difference between different tools/models is the way the failures are presented. In some of the publications, historic operational data is used to estimate the failure behavior. In some cases, failures are assumed to occur after a certain amount of time, the occurrence of failures is therefore modeled in a deterministic way. The mean time between failure (MTBF) can be calculated from observed failures and is often used for this deterministic modeling. In other models, failures occur with a certain probability that is assessed based on collected data, so the failures are occurring randomly according to a defined probability distribution. To model from this probability distribution, samples are drawn from the distribution(s). Popular processes for failure \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Publication** & **Factors Mentioned** \\ \hline [PERSON] and [PERSON] [PERSON] and [PERSON] (1966) & weather (including uncertainty), failure rates, electricity price, price for vessels (costs, fleet composition, types, quantity), workers (shift length, quantity), location of maintenance base, types of maintenance \\ \hline [PERSON] et al. [PERSON] et al. (2017) & component failures, weather conditions, vessels (availability, operational limits, costs), maintenance technicians, repair time, wind farm layout, cost of spare parts, logistics (warehousing and other costs) \\ \hline [PERSON] et al. [PERSON] et al. (2018) & protection methodologies, the occurrence of cable faults and component failures, the repair strategy, wind speed forecasts and condition monitoring systems \\ \hline [PERSON] et al. [PERSON] et al. (2019) & as [PERSON] and [PERSON] [PERSON] (1966) \\ \hline [PERSON] et al. [PERSON] et al. (2010) & climate parameters (wind speed, wave height), transportation systems (weather constraints, mobilization time, charter costs), turbine specific information (power curve, failure distribution), costs \\ \hline [PERSON] and [PERSON] Endrerud and Liyanage (2011) & local weather, turbines, failures, vessels (operational limits, charter costs), costs of spare parts, electricity market price, maintenance crew \\ \hline [PERSON] et al. [PERSON] et al. (2012) & turbines (rating and quantity), distance from shore, wind quality, water depth, accessibility, availability of workers, spare parts, boats and cranes, failure modes \\ \hline [PERSON] et al. [PERSON] et al. (2013) & failure rate of subsystems, delivery time of spare parts, availability of transport vessels, accessibility (weather dependence) \\ \hline [PERSON] and Sorensen [PERSON] and Sorensen (2014) & vessel specific weather limits (wave height, wind speed), weather forecasts, operation failure \\ \hline [PERSON] et al. [PERSON] et al. (2015) & transportation costs, downtime costs, penalty cost for postponed maintenance, technician transfer time, vessel properties (two types), weather conditions \\ \hline [PERSON] et al. [PERSON] et al. (2016) & dynamics of the farm, repair time, spare part stock, interaction among components, accessibility (wave height, wind speed), turbines (rating and quantity), met ocean data, failure distributions, vessel mobilization time \\ \hline [PERSON] and [PERSON] and [PERSON] (2017) & system reliability, cost effectiveness, weather condition, maintenance duration, production loss during maintenance, market electricity price, wind farm location \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. distribution modeling are (homogeneous) Poisson processes (In a Poisson process, the probability of having a certain number \(n\) of (failure) occurrences \(N(t)\) at time t is: \(P(N(t)=n)=\frac{(\lambda t)^{n}}{n!