Abstract
It is a common sense expectation that the efficiency of wind turbines should decline with age, similarly to what happens with most technical systems. Due to the complexity of this kind of machine and the environmental conditions to which it is subjected, it is far from obvious how to reliably estimate the impact of aging. In this work, the aging of five Vestas V52 wind turbines is analyzed. The test cases belong to two different sites: one is at the Dundalk Institute of Technology in Ireland, and four are sited in an industrial wind farm in a mountainous area in Italy. Innovative data analysis techniques are employed: the general idea consists of considering appropriate operation curves depending on the working control region of the wind turbines. When the wind turbine operates at fixed pitch and variable rotational speed, the generator speed-power curve is studied; for higher wind speed, when the rotational speed has saturated and the blade pitch is variable, the blade pitch-power curve is considered. The operation curves of interest are studied through the binning method and through a support vector regression with a Gaussian kernel. The wind turbine test cases are analyzed vertically (each in its own history) and horizontally, by comparing the behavior at the two sites for the given wind turbine age. The main result of this study is that an evident effect of aging is the worsening of generator efficiency: progressively, less power is extracted for the given generator rotational speed. Nevertheless, this effect is observed to be lower for the wind turbines in Italy (order of −1.5% at 12 years of age with respect to seven years of age) with respect to the Dundalk wind turbine, which shows a sharp decline at 12 years of age (−8.8%). One wind turbine sited in Italy underwent a generator replacement in 2018: through the use of the same kind of data analysis methods, it was possible to observe that an average performance recovery of the order of 2% occurs after the component replacement. It also arises that for all the test cases, a slight aging effect is visible for higher wind speed, which can likely be interpreted as due to declining gearbox efficiency. In general, it is confirmed that the aging of wind turbines is strongly dependent on the history of each machine, and it is likely confirmed that the technology development mitigates the effect of aging.
Highlights
It is a well-known fact that machines and technical systems are affected by aging [1,2,3,4,5,6], but it is difficult to theoretically estimate this kind of effect
The methodology for the analysis of the operation curves is inspired by the recommendations of the International Electrotechnical Commission (IEC) [27] as regards the power curve, which is the observed relation between the wind intensity v and the power output P
This results in the fact that the intensities are respectively 6.2 m/s (IRE) and ITA wind turbines have comparable performance up to when the wind turbines are aged 12 years, and at 12 years, the performance of the ITA wind turbines is clearly better than the IRE one
Summary
It is a well-known fact that machines and technical systems are affected by aging [1,2,3,4,5,6], but it is difficult to theoretically estimate this kind of effect. The power production of a wind turbine has a very complex dependence on ambient conditions [7,8,9,10], on the stochastic nature of the source [11,12], on the working parameters [13,14], on the wake interactions [15,16], and on the health status and on the efficiency [17,18] of the sub-components On these grounds, it is a common sense expectation that the efficiency of wind turbines declines with age, but there are no standards about how much and in how much time the performance should decline. It should be noticed that in wind energy practice, it has become common to refurbish wind turbines by adopting aerodynamic and/or control technology innovations [21]; in general, it should be concluded that without knowing in detail the history of the analyzed wind farms, it is likely that the effects of aging are incorrectly estimated
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