# New Tendencies in Wind Energy Operation and Maintenance
## 3 Overview of Wind Farm O&M Research
### Condition-Monitoring System (CM)  
As discussed in the previous sections, the technical availability of wind turbines is high; however, this is due to fast and frequent maintenance, and not a good reliability level and maintenance management. Failures in larger turbines are greater so the condition-monitoring system will need to be improved [36] in order to improve the reliability of the farms. Usually, wind turbines are acquired through after-sales service contracts lasting between 2 and 5 years, which include guarantees and preventive and corrective maintenance that could be adopted after the expiry of the contract period. However, severe unexpected failures can appear between scheduled maintenance intervals, resulting in the loss of money, time and energy production. Moreover, maintenance due to the occurrence of failures can lead to the catastrophic failure of critical components, producing severe consequences for safety, health and the environment. The increase in the technical availability of wind turbines has to go hand in hand with a greater need for optimal maintenance. A condition-monitoring system could meet the needs of the wind industry for better maintenance management and increased reliability. The CM is considered as one of the best solutions for maintenance problems [57]. It presents a great economic benefit [58] compared to other maintenance strategies and depending on the status of the CM. The CM is a widely used tool for failure detection [59], which minimizes downtime by increasing the reliability of the equipment. This allow us to observe the present state [60] of the wind turbine and send alarm signals. Alarm data require little storage [46] but provide rich information to the CM. With early failure detection, major failures can be prevented [49] and associated costs reduced. The condition-based maintenance enables the generation of more energy by reducing failure rates [61] of the turbines, increasing availability. Different approaches to condition-based maintenance [62] focus on induction machines and drive trains in offshore applications. With the use of control algorithms [63], generating units can be simplified in order to reduce maintenance costs. [PERSON] et al. [64] developed a mathematical approach and a program of a Pareto genetic algorithm to calculate the cost of installation and minimal maintenance of a wind farm with minimal power loss during a fault. The wealth of information and data that are in use during the operation of wind farms in SCADA can be treated with a promising technique [52] based on the use of neural networks, and can detect failures in equipment at an early stage. The treatment development of SCADA data, research on the correlation of data coming from SCADA and quantitatively assessing the state of wind turbines [12] are important for improving the condition-monitoring systems. Data management and how these data [65] can help make decisions, given the information they provide, are very important and very profitable for maintenance. Furthermore, a SCADA data clustering model was trained and evaluated for accuracy through simulations, with the results showing that the proposed model [66] is able to accurately detect the deterioration condition of a turbine. Tools that can be used in the CM may be the data mining algorithms that are used to build models for predicting failures of wind turbines [67], improving the performance of statistical tools based on Kalman filter models [57] and algorithms with manageable solutions [68] of dynamic decision making. The gearboxes are the most expensive subsystem and are responsible for the most inactivity, followed by drive trains [69], which are the biggest challenges for the CM and offer greater opportunities for research. New research in gearboxes is being developed, like in [33], and this is based on a new method of analysis using a CM temperature trend. Another method of improving the CM is the use of several sensors [70] to analyze different oil characteristics.