Deliverable D4.1: Assessment of controller key performance indicators & Guidelines on controller application for the management of existing wind farms

Besides the positive impact wind farm controls have in term of annual energy production of the entire farm, the introduction of these new technologies alters the normal functioning of the single machine and might significantly increase the loads on turbines themselves. In this deliverable the effects of three different wind farm control strategies on the single wind turbine have been analyzed.

The analyses considered three kinds of wind farm controls; wake redirection, active wake mixing and induction control.

During the development of D4.1, CL-Windcon´s partners have investigated the effects on the single wind turbine in terms of Key Performance Indicators (KPIs). As indicators, not only the fatigue loads have been analyzed but also the ultimate loads, i.e. the maximum loads a machine may undergo in case of extreme events, like gusts or failures.

Thanks to this extensive simulation campaign, performed with state of the art aeroelastic simulators, it is possible to assess that the increase of turbine fatigue loads induced by farm controls is limited. On the other side, all the analyses conducted by the partners observed that the impact of wind farm control on ultimate loads on some subcomponents is not negligible as well as the maximum blade deflections. This analysis is performed in subsequent parts of the project.

You can get the complete document from our download section.

Deliverable D2.4: Minimal loading power curtailment control techniques

The purpose of this deliverable is to describe a novel wind farm power curtailment control strategy that aims to distribute the loads evenly among the wind turbines in such a way that the accumulated fatigue loading over the operational lifetime is balanced as much as possible.

To this end, the deliverable report first describes the different types of power curtailment functions at farm level, and their implementation in the wind turbine controllers. Subsequently, the methodology behind the newly proposed loads-balancing power curtailment strategy on farm level is presented.

Some initial simulation results are presented in D2.4. These results indicate that the new wind farm curtailment control algorithm operates properly when coupled with the Fast.Farm software. Later on, in Deliverable D3.5, the curtailment control algorithm has been more extensively validated.

You can get the complete document from our download section.

Deliverable D2.3: Control methodology for induction based control and for wake redirection control

The aim of Work Package 2 of the European CL-Windcon project is the synthesis of wind turbine and wind farm control algorithms that aim to reduce the levelized cost of energy of wind farms. In this deliverable, D2.3, several control solutions were devised to address the problematic, inevitable wake forming in existing wind farms. These control solutions vary significantly; one solution employs a simplified mathematical model to optimize the turbine’s yaw angle enforcing a lateral force on the flow and displacing the wake. A second method focuses on “shaking” the rotor blades to induce turbulence behind the rotor and enhance wake recovery. A third algorithm focuses on multi-objective optimization by combining the minimization of structural loads on the turbines with the maximization of the turbine power extraction. Other solutions address the uncertainty, variability and dynamic nature of the wind and mathematical models, often ignored in the existing literature.

A subset of these algorithms have been tested in further phases of the project through high-fidelity simulations, performing experimental tests at the the Politecnico di Milano wind tunnel, and through field experiments at the Sedini wind farm in Italy.

You can get the complete document from our download section.

Deliverable D4.2: Hardware and infrastructure conditions for energy capture and fatigue improvements

The deliverable reports the effect of the proposed wind farm control algorithms on the operations of existing wind farms in terms of energy capture, hardware and communication infrastructure and power system compliance. Developed control techniques and their application in existing wind farms are analyzed. The impact of these techniques for grid supporting is also studied.

The purpose of the studies developed within Deliverable 4.2 is to maximize the energy production, as well as balancing loads following key performance indicators which evaluate and quantify the performance of the system.

The report is structured in seven chapters. The first two chapters summarize and highlight the objectives to be accomplished and the purpose of the task. The definition of key performance indicators for evaluation and quantification of the performance of the system are presented in chapter 3. The fourth part is focused on the necessary hardware and communication infrastructure for the application of wind farm control methods developed in WP2. The impact of the control methods on wind farms is analyzed in chapter number 5. The sixth division of the deliverable studies the impact of de-rating in power system response. Finally the last part of D4.2 emphasizes the main achievements, comparing them to the initial objectives of the deliverable, concluding with the accomplishments of the goals of the report.

During the development of this report the applicability of the project´s control strategies to real world wind farms is assessed, and it is started to understand their effects on farm efficiency and grid integration.

You can get the complete document from our download section.

CL-Windcon at the American Control Conference 2019

The American Control Conference (ACC) is an annual 3-day meeting that brings together an international community of researchers and practitioners in all areas related to the engineering and science of control systems. In this occasion the event was held in Philadelphia from the 10th to the 12th of July and comprised several types of presentations in regular and invited sessions, tutorial sessions, and special sessions along with workshops and exhibits.

CL-Windcon was present in the conference thanks to our partners from TU-Delft, Bart Doekemeijer and Jan-Willem van Wingerden. They participated with one tutorial session about “Closed-Loop Wind Farm Control” where they showed some of the progress of the CL-Windcon project. The last day of the ACC 2019 our partners also participated in an invited session in which the topic was “Distributed Wind Farm Control and Related Applications”.

Deliverable D3.4: Testing in the wind tunnel of wind turbine controllers

Wind tunnel experiments play an essential part in the study of the flow within a wind farm. The quantity and the quality of measurements achievable in a wind tunnel, along with the possibility to repeat a single experiment in a controlled environment whose boundary conditions are well-known, renders the wind tunnel experiments extremely valuable to generate data to be used for validating mathematical models.

The wind tunnel testing activities performed during the first two years of CL-Windcon project are the object of this deliverable. These testing campaigns were executed in the Politecnico di Milano installations with the goal of providing the project partners, and the international wind energy community, with valuable experimental data in order to support the development of technologies related to wind farm control.

During the development of this report, a huge amount of experimental data related to the flow within the wake in different ambient conditions, along with the related wind turbine measurements have been obtained and made available to the international community. At the same time, the characteristics of the wake shed by a single wind turbine and by a cluster of two wind turbine models were studied. The effects on the wake recovery, deficit and deflection of different wake control strategies were measured.

You can get the complete document from our download section.

Deliverable D3.3: Demonstration of wind turbine controllers and supporting technologies by simulations

The main objectives of this deliverable have been to support the development of new wind farm control approaches and to provide new measures of wake interactions in order to contribute to an optimized operation of the wind farm. To realize new control strategies on wind farm level, supplementary controller and adaptions to existing controllers are needed. This part of the project presents simulation results using the CL-Windcon baseline controller, supplementary control strategies on wind turbine level as well as observers on turbine level.

The control methodologies that have already been developed during the project are analyzed and simulations are conducted during this process. Firstly, the CL-Windcon wind turbine baseline controller was analyzed at different wind speed conditions. Secondly, different power derating strategies are compared that are later used in the project to realize axial induction control and smart power curtailment in the wind farm. One of the most important points was about the lidar-based closed-loop wake control concept. This was analyzed in frequency domain and simulations are performed to test the adaption of the controller to a disturbance. Finally, a reliability enhancing method is studied in simulation at different wind speeds to show its reaction to specific events.

Altogether, the different analyses and concepts treated during this deliverable have provided new prospects in operating the wind farm in an optimized way. Furthermore they represent an intermediate layer between novel farm control strategies and single wind turbine operation in the wind farm and therefore offer the possibility to leverage new wind farm control strategies.

You can get the complete document from our download section.

Deliverable D1.4: Classification of control-oriented models for wind farm control applications

The design and operation of a wind farm must account for physical phenomena that can be typically neglected when it comes to stand-alone machines. Indeed, complex interactions take place between the atmospheric flow and the wind farm, as well as within the wind power plant itself.

In this deliverable the classification and the analysis of the wake models developed during the CL-Windcon project are exposed. The models are classified with respect to their capabilities with various aspects. In the first part, D1.4 gives a general overview on each model as well as a written classification description. Then a spreadsheet is presented which analyzes and compares the models with their dedicated validity. In the following chapters different aspects of validation and tuning are studied. Calibration and parameter tuning are described in detail with the FLORIS and the WFSim model. A sensitivity study is performed using the FAST.Farm model analyzing the impact and uncertainty of different parameters.

One important point of the deliverable is the presentation of a technique for the management of sensor failure, which may have applications in wind farm control, where sensor redundancy may not only happen by design, but also by collaboration between turbines.

You can get the complete document from our download section.

Deliverable D3.2: Definition of field-testing conditions

Deliverable 3.2 defines and describes the full scale field tests being executed at Enel Green Power´s (EGP) wind farm in Sedini (Italy) as part of Task 3.3 “Demonstration by full-scale testing” under WP3 “Demonstration and validation of prototypes”. An extensive field campaign has been planned at this site to investigate wake characterization for improved modelling of turbine interaction and demonstration of closed loop control, taking wind turbine couplings into account. The planned field tests comprise two types of experiments: first, simple turbine performance, loads and wake characterization for operation with yaw misalignment and reduced induction. These tests will allow to characterize the modelling space and to validate the simplified control oriented system models. The second type of test will be demonstration of farm control algorithms, where the newly developed algorithms are tested and validated against baseline performance.

All the experiments mentioned above were executed in a so-called toggle mode, i.e. switching between experiment mode and baseline mode in 30-60 minute intervals. This allows for similar environmental conditions and therefore better comparison between baseline and altered conditions. Also, during the experiments, selected turbines operated outside of this normal operation mode to ensure that the turbine operation was safe during all times.

Summarizing, this deliverable gives a detailed description of the planned field test. Based on the outcomes of WP1 and WP2, two types of experiments are proposed. The additional instrumentation that is needed for a thorough evaluation is described in detail in this deliverable as well as the expected time line.

You can get the complete document from our download section.

PhD position on numerical and experimental modelling of “Off-Shore Wind Farm Control”

The Department of Mechanical Engineering of Politecnico di Milano and the Delft Centre for Systems and Control of the Technical University of Delft offer one doctoral position in the wind energy field.

These two centres have a strong collaboration in the wind energy field that recently led to the participation in several European projects. The first one, Polimi, has a strong experimental background, with the availability of a large wind tunnel infrastructure and several wind turbine models and scale model wind turbine design procedures. A hardware-in-the-loop framework is adopted to test floating wind turbines installed on the Hexafloat platform for the modelling of the floating wind turbine platform. High fidelity numerical tools (CFD LES actuator line) for wind farm modelling validated vs experiment are also available. The second one, TUDelft, has a strong background on control topics, having specific experience on Wind farm control, Smart dynamic wind turbine control, Data driven control and Nonlinear system identification, with the availability of several reliable and validated numerical models for wind turbines wakes dynamics and interaction with the downwind turbines. Several strategies to minimize LCOE have been developed, acting on wake redirection, induction reduction, minimizing dynamic loading.

What they are looking

A young scientist with a Master Degree latest on 31st October 2019 able to understand concepts quickly, following a sharp learning curve, with good planning skills and the ability to work independently. This person will have enthusiasm, the willingness to exploit his skills in ocean sciences research for the benefits of communities pursuing sustainable growth, and be seeking the opportunity to develop his research career through scientific publications. He will be able to actively participate in the R&D activities of collaborative research projects, both at a national and international level, and have a desire to become a future leader in your field.

Skills and qualifications will include:

– As minimum education requirement, a University Degree (MSc or equivalent) in Science, Technology, Engineering and Mathematics (STEM) disciplines: Physics, Mathematics, Engineering.

– Some experience in computational sciences or statistics, with knowledge and proved use of scientific programming languages or advanced software environments (e.g. Fortran, C, Python, Matlab).

– Fluent English (the list of accepted certificates and levels is available in the call for application).

About the role:

The typical installation of wind turbines is realizing a cluster of closely spaced turbines to minimize the infrastructure cost. The close space between the rotors leads to the interaction between wind turbine wakes, leading to reduction in energy harvesting and undesired fatigue loads. A wind turbine operating in waked condition has a reduction in extracted power, since the upstream rotor reduces the amount of energy of the incoming wind. Moreover the rotor loads may not be uniform, in time and space, due to the mean boundary layer gradient growth and turbulence large size coherent structures. Such dynamic excitation issues are even more critical, given the intrinsic and growing large size of offshore wind turbines, leading to possible fatigue damage and large dynamics of the very flexible blades . The very peculiar dynamics of the floating substructures is giving additional relevance to the control issues of the specific single Wind Turbine in terms of stability and dynamic response to wave excitation. The development of reliable and validated numerical methods suitable for modelling the wind turbines wakes dynamics and interaction with the downwind turbines became the fundamental tool for designing optimized solutions of wind farm control, exploiting innovative wind farm control strategies, acting on wake redirection, induction reduction and other techniques finalized at limiting the wind turbines dynamic loading and at the same time increasing the overall wind farm production.

It is fundamental to develop control strategies, designed together with the structure components, capable of capturing the dynamics of the wake of offshore wind farms, regulating the produced power and reducing fatigue loads, enhancing, as consequence, the machine life and reducing the LCOE.


– Position: Double PhD, Full Time

– Duration: 48 Months

– Salary: € 15.900 net/year (will increase during the years) + travel allowance + research budget

– The position will have as possible Supervisors:


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How to apply:

Application procedure is available at required documents (project proposal, CV, motivation letter etc.) must be uploaded online following the instructions provided at the same link.

For further information on application procedure please contact  or Preliminary enquires during the application process are strongly suggested.

Deadline: 21th May 2019 (2pm, Italian time).