# New Tendencies in Wind Energy Operation and Maintenance
## 3 Overview of Wind Farm O&M Research
### Maintenance Strategies (M_stra)
Moreover, the number of failure predictions is useful for planning reserves of the key components [87] for multiple devices running simultaneously and optimizing the support strategy of rational maintenance. [PERSON] et al. [88] presented a stochastic optimization model for opportunistic maintenance services of offshore wind farms; optimization is performed on a daily basis to update maintenance planning based on current production and weather. [PERSON] et al. [89] developed a methodology to develop reduced-order models to support the operation and maintenance of offshore wind turbines. In [90], an opportunistic policy is proposed, which is defined by three decision parameters to coordinate the various maintenance activities and minimize the maintenance cost rate, through a comparative study with the policy of corrective maintenance, and it shows the advantage of the method of opportunistic maintenance work to significantly reduce maintenance costs. The development of advanced techniques to detect [9] the appearance of mechanical and electrical failures of wind turbines at a sufficiently early stage is very important for maintenance actions. Future maintenance strategies [21] provide the most relevant functional failures and reveal their causes, to identify corrective measures to avoid breakdowns as much as possible. At the same time, future strategies for diagnosing faults are identified by a qualitative fault tree analysis in [59]. Another maintenance strategy to consider is that which takes into account research in which life cycle inventories of a conceptual offshore wind farm are developed and assessed using a hybrid life cycle assessment methodology [91]. [PERSON] et al. [92] have presented an analysis of the transport strategy, a queuing model of maintenance activities and an economic model of the maintenance support organization. It is also important to analyze the degradation cycle costs, maintenance and life for various coating systems [93; 94] for offshore wind farms, and the maintenance of the blade as a whole [95]. The planning and scheduling of maintenance operations are decisive for wind farm availability and a key component of the operational costs. A challenging real-world application of integrated planning and scheduling is shown in [96], which formulates this problem as a Planning Domain Definition Language (PDDL). At present, the maintenance scheduling focuses on the field of optimization of a single object, and [97] proposes a model of multi-objective optimization for maintenance scheduling of a transmission network, with the aim to reduce maintenance costs and the minimum expected energy that is not supplied, and the research developed a method of interactive multi-objective decision making. Another improvement proposal [98] for the maintenance schedule is a model that takes into account the peak regulation pressure balance for a power system and to have a fair value, the proposed model is solved by a genetic algorithm. [PERSON] et al. [39] proposed a model that extends classical maintenance scheduling to include forecasted market prices and weather conditions, and this proposed maintenance schedule optimization occurs at a discounted cumulative profit of a wind generation portfolio in a fixed-time horizon. For generator maintenance scheduling of large-scale wind power [38] integration, considering peak shaving and based on Benders decomposition, a model is divided into two parts: the main problem and three sub-problems of decentralized coordination, and this method is applied to the maintenance schedule for all generators in the test system. Studies have proposed programming formulations for the problem of the optimization of maintenance schedules, and the scheduling problem [99, 100] is modeled and solved as a mixed-integer linear program. Proposals for a generic wind power forecasting framework without regional or temporal restriction also take into account [101] the effect of energy lost in downtime imposed by scheduled maintenance and external disturbances.