Ludovico Terzi

IEA-Task 31 WAKEBENCH: Towards a protocol for wind farm flow model evaluation. Part 2: Wind farm wake models

Researchers within the International Energy Agency (IEA) Task 31: Wakebench have created a framework for the evaluation of wind farm flow models operating at the microscale level. The framework consists of a model evaluation protocol integrated with a web-based portal for model benchmarking (www.windbench.net). This paper provides an overview of the building-block validation approach applied to wind farm wake models, including best practices for the benchmarking and data processing procedures for validation datasets from wind farm SCADA and meteorological databases. A hierarchy of test cases has been proposed for wake model evaluation, from similarity theory of the axisymmetric wake and idealized infinite wind farm, to single-wake wind tunnel (UMN-EPFL) and field experiments (Sexbierum), to wind farm arrays in offshore (Horns Rev, Lillgrund) and complex terrain conditions (San Gregorio). A summary of results from the axisymmetric wake, Sexbierum, Horns Rev and Lillgrund benchmarks are used to discuss the state-of-the-art of wake model validation and highlight the most relevant issues for future development.

Analysing wind farm efficiency on complex terrains

Actual performances of onshore wind farms are deeply affected both by wake interactions and terrain complexity: therefore monitoring how the efficiency varies with the wind direction is a crucial task. Polar efficiency plot is therefore a useful tool for monitoring wind farm performances. The approach deserves careful discussion for onshore wind farms, where orography and layout commonly affect performance assessment. The present work deals with three modern wind farms, owned by Sorgenia Green, located on hilly terrains with slopes from gentle to rough. Further, onshore wind farm of Nprrekffir Enge has been analysed as a reference case: its layout is similar to offshore wind farms and the efficiency is mainly driven by wakes. It is shown and justified that terrain complexity imposes a novel and more consistent way for defining polar efficiency. Dependency of efficiency on wind direction, farm layout and orography is analysed and discussed. Effects of atmospheric stability have been also investigated through MERRA reanalysis data from NASA satellites. Monin-Obukhov Length has been used to discriminate climate regimes.

IEA-Task 31 WAKEBENCH: Towards a protocol for wind farm flow model evaluation. Part 1: Flow-over-terrain models

The IEA Task 31 Wakebench is setting up a framework for the evaluation of wind farm flow models operating at microscale level. The framework consists on a model evaluation protocol integrated on a web-based portal for model benchmarking (www.windbench.net). This paper provides an overview of the building-block validation approach applied to flow-over-terrain models, including best practices for the benchmarking and data processing procedures for the analysis and qualification of validation datasets from wind resource assessment campaigns. A hierarchy of test cases has been proposed for flow-over-terrain model evaluation, from Monin- Obukhov similarity theory for verification of surface-layer properties, to the Leipzig profile for the near-neutral atmospheric boundary layer, to flow over isolated hills (Askervein and Bolund) to flow over mountaneous complex terrain (Alaiz). A summary of results from the first benchmarks are used to illustrate the model evaluation protocol applied to flow-over-terrain modeling in neutral conditions.

Numerical and Experimental Methods for Wake Flow Analysis in Complex Terrain

Assessment and interpretation of the quality of wind farms power output is a non-trivial task, which poses at least three main challenges: reliable comprehension of free wind flow, which is stretched to the limit on very complex terrains, realistic model of how wake interactions resemble on the wind flow, awareness of the consequences on turbine control systems, including alignment patterns to the wind and, consequently, power output. The present work deals with an onshore wind farm in southern Italy, which has been a test case of IEA- Task 31 Wakebench project: 17 turbines, with 2.3 MW of rated power each, are sited on a very complex terrain. A cluster of machines is investigated through numerical and experimental methods: CFD is employed for simulating wind fields and power extraction, as well as wakes, are estimated through the Actuator Disc model. SCADA data mining techniques are employed for comparison between models and actual performances. The simulations are performed both on the real terrain and on flat terrain, in order to disentangle the effects of complex flow and wake effects. Attention is devoted to comparison between actual alignment patterns of the cluster of turbines and predicted flow deviation.

Mathematical methods for SCADA data mining of onshore wind farms: Performance evaluation and wake analysis:

Supervisory control and data acquisition (SCADA) systems have become widely diffuse in modern wind energy technology. The slowdown of new installations and the increasing percentage of energy entering the grid from renewable stochastic sources has diverted attention to the careful optimization of operating farms. Elaborating the complex data stream from SCADA systems into knowledge poses technological and scientific challenges. SCADA data analysis therefore lies at the crossroads of mechanical engineering, applied mathematics, statistics and physics. In the present work, mathematical methods are proposed for tackling the complexity of SCADA data. This idea is to elaborate simplified and more powerful data sets through one action: discretization of continuous quantities. The approach is employed for two very different issues: performance evaluation and wake effects analysis, which is investigated from the point of view of power losses, due to the difficulties associated with optimal turbine alignment with the wind. Two indexes for performance evaluation are formulated. Recurrent non-trivial orientation patterns of clusters of turbines are individuated, and the efficiency associated to them is analyzed. The methods are tested on two wind farms situated in southern Italy.

Advanced Data Mining Techniques for Power Performance Verification of an On-Shore Wind Farm

The monitoring of wind energy production is fundamental to improve the performances of a wind farm during the operational phase. In order to perform reliable operational analysis, data mining of all available information spreading out from turbine control systems is required. In this work a Supervisory Control and Data Acquisition (SCADA) data analysis was performed on a small wind farm and new post-processing methods are proposed for condition monitoring of the aerogenerators. Indicators are defined to detect the malfunctioning of a wind turbine and to select meaningful data to investigate the causes of the anomalous behaviour of a turbine. The operating state database is used to collect information about the proper power production of a wind turbine, becoming a tool that can be used to verify if the contractual obligations between the original equipment manufacturer and the wind farm operator are met. Results demonstrate that a proper selection of the SCADA data can be very useful to measure the real performances of a wind farm and thus to define optimal repair/replacement and preventive maintenance policies that play a major role in case of energy production.

A SCADA data mining method for precision assessment of performance enhancement from aerodynamic optimization of wind turbine blades

The target of improving efficiency of wind kinetic energy extraction has stimulated a certain attention to wind turbine retrofitting. This kind of interventions has material and labor costs and producible energy is lost during installation. Further, the estimation of the energy enhancement is commonly provided under the hypothesis of ideal conditions that can be very different from real ones. Therefore, a precise estimation of performance improvement is fundamental. In this work, a SCADA-based method is formulated for estimating the improvement in energy production of multi-megawatt wind turbines, sited in Italy in a very complex terrain. The blades of one wind turbine in the farm have been optimized by installing vortex generators and passive flow control devices. An Artificial Neural Network (ANN) model is employed: the output is the power of the retrofitted wind turbine and the inputs are the powers of some reference nearby wind turbines. The production increase is estimated by observing how the difference between simulated and measured power output changes after the installation of the aerodynamic upgrade. The average improvement is estimated as the 3.9% of the total energy produced below rated power.

Analyzing wind turbine flow interaction through vibration data

Wind turbines commonly undergo non-stationary flow and, not rarely, even rather extreme phenomena. In particular, rough terrains represent a challenging testing ground, because of the combination of terrain-driven flow and wakes. It is therefore crucial to assess the impact of dynamic loads on the turbines. In this work, tower and drive-train vibrations are analyzed, from a subcluster of four turbines of a wind farm sited in a very complex terrain. The main outcome of the study is that it is possible to start from the analysis of wind conditions and interpret how wakes manifest in the vibrations of the turbines, both at structural level (tower vibrations) and at the drive-train level. This wind to gear approach therefore allows to build a connection between a flow phenomenon and a mechanical phenomenon (vibrations) and can be precious to assess loads in different working conditions.

Computing the Real Impact of Wind Turbine Power Curve Upgrades: A SCADA-Based Multivariate Linear Method and a Vortex Generator Test Case

Computing the real impact of wind turbine power curve upgrades: a SCADA-based multivariate linear method and a vortex generator test case Davide Astolfi 1,‡*, Francesco Castellani 1,‡, Mario Luca Fravolini1,‡, Silvia Cascianelli1,‡, Ludovico Terzi 2,‡ 1 University of Perugia Department of Engineering, Via G. Duranti 93 06125 Perugia (Italy); davide.astolfi@unipg.it; francesco.castellani@unipg.it; mario.fravolini@unipg.it; silvia.cascianelli@unipg.it 2 Renvico srl, Via San Gregorio 34, Milano 20124, Italy; ludovico.terzi@renvico.it * Correspondence: davide.astolfi@unipg.it; Tel.: +39 075 585 3709 ‡ These authors contributed equally to this work.

Innovative methods for wind turbine power curve upgrade assessment

Wind turbine power curve upgrades have recently been attracting considerable investment in the operation of wind farms and noticeable attention in the wind energy literature. Due to the non-stationary conditions to which wind turbines are subjected, the most consistent strategy for quantifying the production improvement from the installation of an upgrade is comparing, after at least some months of operation, the post-upgrade production against a model of the pre-upgrade production under the same conditions. Formulating adequate models for the power of the upgraded wind turbines is in general non-trivial and it can be difficult to achieve the precision that wind turbine practitioners typically require for the production assessment. In the present work, a multivariate linear method for selecting the most appropriate input for modeling a given output is presented and applied to a test case. The test case is a multi-megawatt wind turbine owned by Renvico, on whose blades vortex generators and passive flow control devices have been installed. Applying the proposed method, it is possible to compute with precision the production improvement in the first five months of post-upgrade operation (purely aerodynamic upgrade) and in the subsequent three months (aerodynamic and control upgrade). It is therefore possible to appreciate the different contributions to the production enhancement from the aerodynamic and control improvement. A non-upgraded wind turbine from the same wind farm is also studied and the precision of the results inspires the use of the proposed method for performance control and monitoring in general.