Francesco Castellani

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.

Wind Power Forecasting techniques in complex terrain: ANN vs. ANN-CFD hybrid approach

Due to technology developments, renewable energies are becoming competitive against fossil sources and the number of wind farms is growing, which have to be integrated into power grids. Therefore, accurate power forecast is needed and often operators are charged with penalties in case of imbalance. Yet, wind is a stochastic and very local phenomenon, and therefore hard to predict. It has a high variability in space and time and wind power forecast is challenging. Statistical methods, as Artificial Neural Networks (ANN), are often employed for power forecasting, but they have some shortcomings: they require data sets over several years and are not able to capture tails of wind power distributions. In this work a pure ANN power forecast is compared against a hybrid method, based on the combination of ANN and a physical method using computational fluid dynamics (CFD). The validation case is a wind farm sited in southern Italy in a very complex terrain, with a wide spread turbine layout.

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.

Wakes Calculation in a Offshore Wind Farm

This paper is focusing on the wake modeling in large offshore wind farms. Over the sea the ambient turbulence is much lower than onshore, wakes persist for long distances, mixing in a complex pattern. Hence an accurate evaluation of the wakes become crucial in the estimation both of the production and loads. Regarding loads the main effect of the turbines wakes is an increase of turbulence compared to the ambient one. A careful assessment of the wakes is therefore required when analyzing offshore wind farms. The approaches in estimating the wake losses go from simple theoretical or empirical laws to full rotor aerodynamic calculations; in between there is a range of intermediate calculations. Two approaches will be presented: the use of relative simple equations (also called analytical models) which are the standards in the wind resource assessment and a more accurate, but computationally more demanding, the actuator disc technique.

A new technique to improve expected aep estimation in very complex terrain

Annual Energy Production (AEP) estimation is a key issue in the development and financing of wind projects; it is fundamental not only for investment evaluation but also in defining the plant lay-out and dimensions. In order to obtain reliable results, detailed analysis of the wind conditions is needed; for wind sites in complex terrains each stage of this analysis is more difficult than in flat land because of the particular wind field conditions that are characterized by high turbulences and different shapes of the wind profile. It is not well known how these conditions affect wind turbine operation; as a result wind energy production can be different than expected. This is mainly due to the misunderstanding of the wind flow characteristics and the turbine efficiency. The present work is focused on a new method which takes into account the effect of orography in terms of available energy rather than the efficiency for the conversion. A technique to customize power curve is proposed and applied with success in the test case of Fossato di Vico (ITALY) wind farm bringing error in estimating AEP below 10%. Present work has been carried out with Anemon s.p.a. (ITALY) which provided turbine production data and participated in the measuring campaign.

ADVANCED AERODYNAMICS METHODS FOR WIND SITE SELECTION

Wind potential — in Italy — has not been completely explored yet, and many investors are still looking into the most promising sites. The interest on the exploitation of a wind site is linked to the possibility of setting up an industrial plan that yields a fast return on investment. The success of this investment depends on the following parameters: • the amount of funding to be spent (cost of the electric lines, roads, turbines, etc.); • the quality of the predicted wind flow; • the price of the electric energy produced. To select a wind site in a fast and convenient way some of the traditional methods of aerodynamics can be borrowed, such as those related to vehicle dynamics. This paper investigates and compares wind site characterization tools and methodologies based on aerodynamics. Simulations and experimental activities were performed in geographical sites located in the center of Italy, where the complex orography requires efficient methods for site characterization and selection, with the aim of speeding up the start-up of wind turbine installations.Copyright