# New Tendencies in Wind Energy Operation and Maintenance
## 4 Discussion
### LCOE Wind Energy vs. Research on Wind Farm Maintenance  
The O&M costs are a significant part of the LCOE of wind energy. O&M costs can represent between 11% and 30% of the LCOE of a wind project [3; 4]. To be able to compare these LCOE and O&M cost values with the evolution of research in the maintenance of wind farms, it is necessary to perform a multiple linear regression model. In such a model, the regression function that relates the dependent variable (research articles, Y) to the different independent variables (M_COST, M_OPT, C_M, RELI, FAIL, M_STRA and O&M) obeys the following equation:  
\[Y=\beta_{0}M_{COST}+\beta_{1}M_{OPT}+\beta_{2}C_{M}+\beta_{3}RELI+\beta_{4}FAIL \ +\beta_{5}M_{STRA}+\beta_{6}O\&M \tag{1}\]  
where \(\beta_{1}\), \(\beta_{2}\),... \(\beta_{6}\) represent the weight of each independent variable in the global value of the dependent variable Y. As the evolution of all independent variables is known, it is possible to use IBM SPSS Statistics 25\({}^{\text{\textcircled{P}}}\) software for information processing and the development of the multiple regression model. By knowing the evolution of all the variables, the multiple regression model obtains a determination coefficient (R2)--which allows for knowing if the goodness of the model's fit is perfect, i.e., equal to 1. The equation for the evolution of maintenance research on wind farms is as follows:  
\[Y=0.256M_{COST}+0.251M_{OPT}+0.235C_{M}+0.145 RELI+0.094 FAIL+0.018M_{STRA}+0.015 O&M \tag{2}\]  
According to the multiple linear regression model (Equation (2)) and as can already be seen in Figure 3, the highest weight (\(\beta\)) is obtained by the keyword groups M_OPT and M_COST, which shows that these are the groups that contribute the most research on the maintenance of wind farms. In contrast, the groups of variables with the least influence on research into wind farm maintenance are M_STRA and O&M. Despite the fact that, in general, FAIL has usually been one of the less important keywords in research work related to wind energy, it seems that the trend has been changing in recent years. In this sense, in the case of the keyword \"failure\" (FAIL group, Figure 4e), the results obtained have grown considerably, going from 200 results in 2018 to over 500 in 2019, which is more than double the results obtained in one year. Taking 2007 as a reference year, the results obtained for the keyword \"failure\" (FAIL group, Figure 4e) have increased more than ten-fold with respect to 2019. Three different periods can be observed in the evolution of wind farm maintenance research between 2007 and 2019 (Figure 5):  
* 1 st Stage (growth): In 2007, the results obtained were less than 1000. Up to 2014, research in the field grows year by year from 785 results in 2007 to more than 3500 papers in 2013. This stage of growth in research into O&M costs in wind farms is taking place in parallel with the increase in wind projects around the world. At this stage, the scientific and technical interest in the technology was at its highest. * 2 nd Stage (stagnation): After years of constant growth, research into the maintenance of wind farms stagnates between 2014 and 2016. This period of stagnation in research into O&M costs in wind farms was influenced by the global economic crisis which began in 2007. * 3 rd Stage (growth): From 2016 onwards, research in the field increases again year by year, from more than 4000 papers in 2016 to almost 6300 results in 2019. This new phase of growth in O&M cost research is related to the growth of a social conscience in favor of the sustainability of the planet and increasing concern about the consequences of climate change. With all these data, it is possible to reflect on the evolution of the LCOE and O&M and compare them with the evolution of research on wind maintenance farms. As can be seen in Figure 6, in the period 2007-2019, research into wind farm maintenance increased by 87%, while the LCOE of wind projects decreased by 55%.