Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis

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.

A Review of Life Extension Strategies for Offshore Wind Farms Using Techno-Economic Assessments

“…As shown in Figures 5 and 6, the use of existing offshore wind infrastructure for electricity production through life extension and repowering at the end of initial design life is a more cost-effective option compared to the commissioning of a new offshore wind farm. This observation is in agreement with those reported by other researchers, e.g., [48,49]. The analysis performed in this study on London Array wind farm shows that by extending the operational life of the existing wind farm using turbines of the same capacity or retrofitted turbines of higher capacities, a significant saving of up to £2 billion can be made in the capital investments.…”

SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

“…At the beginning of March 2018, the target wind turbine (named Tar in this study) experienced electrical damage at the generator, in consequence of which the generator had to be replaced. In [21], a study was devoted to the analysis of the performance of the Tar wind turbine before and after the generator replacement, in comparison to the other wind turbines in the farm. The objective of that study was the assessment of the impact of the generator aging on wind turbine performance.…”

“…The result was that after the generator replacement the performance of Tar slightly recovered, while the performance of the rest of the wind farm kept slightly worsening due to the effect of aging. In [21], only the main operation variables were analyzed.…”

“…The measurements used for this study were: Section 3.3 features a detailed discussion about how the data sets were arranged for the diagnosis of the damage at the Tar wind turbine and for the comparison against a healthy reference (named Ref). For completeness, Tar and Ref correspond to ITA4 and ITA3 according to the nomenclature of [21].…”

Perspectives on SCADA Data Analysis Methods for Multivariate Wind Turbine Power Curve Modeling

“…From the discussion of Table 1, it arises that the operation variables, in particular blade pitch and rotor speed, are the most effective additional covariates for a multivariate power curve model. • It has been decided to include the generator speed in the set of input variables because in [15,35] it has been observed that the aging of generator efficiency can affect remarkably the amount of power which is extracted. Wind turbines of the same model can produce different power for the same generator speed.…”