• Energy Research

The long-term analysis of wind turbine performance is a complex task, because the extracted power has a multivariate dependence on ambient conditions and working parameters, which in general changes during the machine lifetime due to aging effects and-or technology optimization. For this reason, the aim of this study is developing appropriate techniques based on SCADA data analysis. A real-world test case is discussed: a decade (2011–2020) of data has been analyzed for seven 2-MW wind turbines owned by ENGIE Italia, which underwent a control optimization in 2018. Four wind turbines in the long run display even a positive drift, which is due to the control upgrade; two have a slight performance decline with age which is compatible with literature estimates; one wind turbine has a noticeable worsening in time, which is exacerbated rather than compensated by the control optimization. It is therefore argued that the long-term performance trend depends on the history of each wind turbine and the resulting scenario is complex. The control optimization provides the expected improvement on six wind turbines out of seven, but leads to a worsening on the worst wind turbine: therefore, the health status of each machine should be monitored before altering the operation.

The optimization of wind energy conversion efficiency has been recently boosting the technology improvement and the scientific comprehension of wind turbines. In this context, the yawing behavior of wind turbines has become a key topic: the yaw control can actually be exploited for optimization at the level of single wind turbine and of wind farm (for example, through active control of wakes). On these grounds, this work is devoted to the study of the yaw control optimization on a 2 MW wind turbine. The upgrade is estimated by analysing the difference between the measured post-upgrade power and a data driven model of the power according to the pre-upgrade behavior. Particular attention has therefore been devoted to the formulation of a reliable model for the pre-upgrade power of the wind turbine of interest, as a function of the operation variables of all the nearby wind turbines in the wind farm: the high correlation between the possible covariates of the model indicates that Principal Component Regression (PCR) is an adequate choice. Using this method, the obtained result for the selected test case is that the yaw control optimization provides a 1% of annual energy production improvement. This result indicates that wind turbine control optimization can non-negligibly improve the efficiency of wind turbine technology.

Pitch angle control is the most common means of adjusting the torque of wind turbines. The verification of its correct function and the optimization of its control are therefore very important for improving the efficiency of wind kinetic energy conversion. On these grounds, this work is devoted to studying the impact of pitch misalignment on wind turbine power production. A test case wind farm sited onshore, featuring five multi-megawatt wind turbines, was studied. On one wind turbine on the farm, a maximum pitch imbalance between the blades of 4.5 ° was detected; therefore, there was an intervention for recalibration. Operational data were available for assessing production improvement after the intervention. Due to the non-stationary conditions to which wind turbines are subjected, this is generally a non-trivial problem. In this work, a general method was formulated for studying this kind of problem: it is based on the study, before and after the upgrade, of the residuals between the measured power output and a reliable model of the power output itself. A careful formulation of the model is therefore crucial: in this work, an automatic feature selection algorithm based on stepwise multivariate regression was adopted, and it allows identification of the most meaningful input variables for a multivariate linear model whose target is the power of the wind turbine whose pitch has been recalibrated. This method can be useful, in general, for the study of wind turbine power upgrades, which have been recently spreading in the wind energy industry, and for the monitoring of wind turbine performances. For the test case of interest, the power of the recalibrated wind turbine is modeled as a linear function of the active and reactive power of the nearby wind turbines, and it is estimated that, after the intervention, the pitch recalibration provided a 5.5% improvement in the power production below rated power. Wind turbine practitioners, in general, should pay considerable attention to the pitch imbalance, because it increases loads and affects the residue lifetime; in particular, the results of this study indicate that severe pitch misalignment can heavily impact power production.

Abstract SCADA control systems are the keystone for reliable performance optimization of wind farms. Processing into knowledge the amount of information they spread is a challenging task, involving engineering, physics, statistics and computer science skills. This work deals with SCADA data analysis methods for assessing the importance of how wind turbines align in patterns to the wind direction. In particular it deals with the most common collective phenomenon causing clusters of turbines behaving as a whole, rather than as a collection of individuality: wake effects. The approach is based on the discretization of nacelle position measurements and subsequent post-processing through simple statistical methods. A cluster, severely affected by wakes, from an onshore wind farm, is selected as test case. The dominant alignment patterns of the cluster are identified and analyzed by the point of view of power output and efficiency. It is shown that non-trivial alignments with respect to the wind direction arise and important performance deviations occur among the most frequent configurations.

Abstract Wind turbines are an energy conversion system having a low density on the territory, and therefore needing accurate condition monitoring in the operative phase. Supervisory Control And Data Acquisition (SCADA) control systems have become ubiquitous in wind energy technology and they pose the challenge of extracting from them simple and explanatory information on goodness of operation and performance. In the present work, post processing methods are applied on the SCADA measurements of two onshore wind farms sited in southern Italy. Innovative and meaningful indicators of goodness of performance are formulated. The philosophy is a climax in the granularity of the analysis: first, Malfunctioning Indexes are proposed, which quantify goodness of merely operational behavior of the machine, irrespective of the quality of output. Subsequently the focus is shifted to the analysis of the farms in the productive phase: dependency of farm efficiency on wind direction is investigated through the polar plot, which is revisited in a novel way in order to make it consistent for onshore wind farms. Finally, the inability of the nacelle to optimally follow meandering wind due to wakes is analysed through a Stationarity Index and a Misalignment Index, which are shown to capture the relation between mechanical behavior of the turbine and degradation of the power output.

The electric generator is estimated to be among the top three contributors to the failure rates and downtime of wind turbines. For this reason, in the general context of increasing interest towards effective wind turbine condition monitoring techniques, fault diagnosis of electric generators is particularly important. The objective of this study is contributing to the techniques for wind turbine generator fault diagnosis through a supervisory control and data acquisition (SCADA) analysis method. The work is organized as a real-world test-case discussion, involving electric damage to the generator of a Vestas V52 wind turbine sited in southern Italy. SCADA data before and after the generator damage have been analyzed for the target wind turbine and for reference healthy wind turbines from the same site. By doing this, it has been possible to formulate a normal behavior model, based on principal component analysis and support vector regression, for the power and for the voltages and currents of the wind turbine. It is shown that the incipience of the fault can be individuated as a change in the behavior of the residuals between model estimates and measurements. This phenomenon was clearly visible approximately two weeks before the fault. Considering the fast evolution of electrical damage, this result is promising as regards the perspectives of exploiting SCADA data for individuating electric damage with an advance that can be useful for applications in wind energy practice.

Wind energy represents a promising alternative to replace traditional fossil-based energy sources. For this reason, increasing the efficiency in the conversion process from wind to electrical energy is crucial. Unfortunately, the presence of systematic errors (mostly related to the yaw and pitch angles) is one of the key factors causing underperformance, and for this reason, it requires adequate identification. The present work deals with diagnosing wind turbine static yaw error, occurring when the wind vane sensor is incorrectly aligned with the rotor shaft. A thorough investigation methodology is proposed by considering a unique experimental test-up shared by the Eolos Wind Research Station. A utility-scale wind turbine has been imposed to operate subjected to several static yaw errors and reference meteorological data collected nearby the wind turbine were available. By analyzing the relation between the meteorological data and the SCADA data collected by the wind turbine, a systematic alteration in the measurements of nacelle wind speed in the presence of the yaw error is explicitly shown. This phenomenon has been overlooked in the literature and leads to revisiting the methods mostly employed for the diagnosis of the error. Furthermore, a correlation between the presence of static error, increased blade pitch, and heightened levels of tower vibration is observed. In summary, this work provides a comprehensive characterization of the experimental evidence associated with the presence of a wind turbine static yaw error. This paves the way for more effective diagnostic techniques for wind turbine yaw errors, potentially revolutionizing data-driven maintenance strategies.

Wind turbine upgrades have recently been spreading in the wind energy industry for optimizing the efficiency of the wind kinetic energy conversion. These interventions have material and labor costs; therefore, it is fundamental to estimate the production improvement realistically. Furthermore, the retrofitting of the wind turbines sited in complex environments might exacerbate the stress conditions to which those are subjected and consequently might affect the residual life. In this work, a two-step upgrade on a multimegawatt wind turbine is considered from a wind farm sited in complex terrain. First, vortex generators and passive flow control devices have been installed. Second, the management of the revolutions per minute has been optimized. In this work, a general method is formulated for assessing the wind turbine power upgrades using operational data. The method is based on the study of the residuals between the measured power output and a judicious model of the power output itself, before and after the upgrade. Therefore, properly selecting the model is fundamental. For this reason, an automatic feature selection algorithm is adopted, based on the stepwise multivariate regression. This allows identifying the most meaningful input variables for a multivariate linear model whose target is the power of the upgraded wind turbine. For the test case of interest, the adopted upgrade is estimated to increase the annual energy production to 2.6 ± 0.1%. The aerodynamic and control upgrades are estimated to be 1.8% and 0.8%, respectively, of the production improvement.