# New Tendencies in Wind Energy Operation and Maintenance
## 3 Overview of Wind Farm O&M Research
### Failure Rate and Analysis (FAIL)  
The technological development of wind energy has favored more complex processes, so the failure rate of systems is increasing and a strategy to model reliability and optimize the maintenance of wind power generation systems is needed. It is essential to determine and record the main underlying cause of failure events; data logging and statistical processing of information failures [20] could improve the reliability of the system components. For instance, the results of the failure statistics and evaluation of expert opinion [21] were focused on the most critical subsystems with respect to the failure frequency and its consequences: the gearbox, generator, electrical system and the hydraulic system. [PERSON] [20] proposed a remote instrument monitoring system, which constitutes of a wireless sensor network-based supervisory control and data acquisition (SCADA) system, with multiple sets of sensors distributed widely across each turbine in the wind farm. This not only helps to monitor the health of the turbines but also helps to take preventive action before critical and catastrophic failures take place. [PERSON] and [PERSON] [8] explored the data failure provided by supervisory control and data acquisition and provided failure prediction at three levels: failure and no failure prediction, severity and prediction of a specific failure. For each level, emerging failures were expected between 5 and 60 min before they occurred and several data mining algorithms were applied to develop predictive models of failures. Modern onshore wind turbines achieve a high availability of 95-99%. However, these values only give information from the time that wind turbines are in operation and the energy produced was not considered when compared to its theoretical production and the availability based on energy availability differs when it is based on time. To investigate this difference, [PERSON] et al. [21] examined the effect of speed failure rates depending on wind energy production by the analysis of turbines subjected to different wind speeds to determine the the energy loss due to the downtime of the turbine. Furthermore, another study [22] determined that failures are classified into three classes according to downtime; the gearbox and the generator have the same amount of downtime, while the highest numbers of failures are in the pitch and the electrical system. In this paper, a discrete event model was developed to simulate the operation, the occurrence of failures and the maintenance of onshore wind farms. To reduce these downtimes of the gearbox, some works [23] performed a prognostic of its failures by utilizing robust multivariate statistical techniques with existing SCADA data on wind farms. [PERSON] et al. [24] took a doubly fed generator and, based on the mathematical model of the Markov process and the theory of reliability, built a model of reliability for wind power generation. [PERSON] et al. [25] developed model maintenance options, developing a hybrid methodology based on Monte Carlo simulations and decision trees for a cost-benefitrisk analysis of prognostics and health management. The methodologies in this paper address the fundamental objective of system maintenance with prognostics: to maximize the use of the remaining useful life while concurrently minimizing the risk of failure, showing the value of having a prognostics system for gearboxes and determining the value of waiting to perform maintenance. Mechanical failures of wind turbines represent a significant cost in terms of both repairs and downtime. In this sense, [PERSON] et al. [26] proposed a Hilbert-Huang transform (HHT)-based algorithm to effectively extract fault signatures from generator current signals for wind turbine fault diagnosis. [PERSON] et al. [27] sought to open a new panorama of knowledge in understanding the actual behavior of offshore wind turbines in the marine environment and proposed the creation of a database to help with design to operation. Finally, [PERSON] et al.