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Advanced integrated supervisory and wind turbine control for optimal operation of large Wind Power Plants

Deliverables

Simple dynamic wind farm model

Report describing dynamic model with testing and validation results. the deliverable is referring to task 1.3.3. Measure of success: Simulations run rapidly enough for iterative tuning of relevant control parameters; consistency of 10-minute statistics compared to SCADA data. Participants actions: DNV is solely responsible.

Dissemination and communication plan and annual report on the dissemination and communication activities, 2

The dissemination plan delivered in M12 (D5.6) will be monitored and updated. The dissemination and communication activities conducted in M12-24 will be summarized in an annual report to record and assess the progress in the project promotion. The annual internal workshop held within the consortium within the second year of the project will be reported. The deliverable is referring to task 5.1 and 5.3. Measure of success: a document with the dissemination plan and workshops summarizing the dissemination activities including workshops and training activities. Participant actions: DTU (with the support of all partners) will prepare a detail document with the dissemination plan and the annual report.

Wind field measurements using LiDAR

Perform measurement campaign. Perform sanity check on the dataset of LiDAR and turbine instrumentation data to be distributed to partners. The deliverable is referring to task Measure of success: Deliver report & data. Participant actions: DTU and ORE will in coorperatin check data. DTU is responsible for the report.

Coupled Gaussian wake-merging model

Fast wake model coupled to a ABL response model, and report with testing and validation results. The deliverable is referring to task 1.3.2. Measure of success: Improved accuracy over standard wake model in particular in regimes with strong ABL coupling. Participants actions: KUL only actor

First project video

Each year a short video (24 min) will be produced presenting core aspects or achievements of the project. This is the first video. Thedeliverable is refering to task 5.1 and 5.2. Measure of success: 1 of 4 videos available to the public at the TotalControl website. Participants actions: DTU will work with professional videomakers to produce the video. Project partners will contribute to the content and take part in the videos as relevant.

Optimization of reactive power dispatch

Optimization of the reactive power dispatch between the wind turbines so that the total losses are minimized. The deliverable is referring to task 2.3. Measure of Success: Optimization of WPP set points conditioned on grid demands and operating conditions. Participant actions: DTU is solely responsible.

Dissemination and communication plan and annual reports on the dissemination and communication activities, 1

The dissemination plan will be monitored and updated. The dissemination and communication activities conducted in M1-12 will be summarized in an annual report to record and assess the progress in the project promotion. The annual internal workshop held within the consortium within the first 12 month of the project will be reported. The deliverable is referring to task 5.1 and 5.3. Measure of success: a document with the dissemination plan and workshops summarizing the dissemination activities including workshops and training activities. Participant actions: DTU (with the support of all partners) will prepare a detail document with the dissemination plan and the annual report.

Upgrade of Fuga

Upgrade of Fuga for yawed rotors and strongly stable stratification, and report with testing and validation results. The deliverable is referring to task 1.3.1. Measure of success: Accuracy in new regimes with same level as Fuga accuracy in standard regimes. Participants actions: DTU is solely responsible.

Upgrade of DWM

Upgrade of the DMW for non-neutral ABL and turbine yaw control, and report with testing and validation results. The deliverable is referring to task 1.2.5. Measure of success: Level of accuracy reached for the new conditions compared to the current DMW accuracy for neutral non-yawed conditions. Participants actions: DTU is solely responsible.

Control algorithms for primary frequency and voltage support

Report containing a description of the simulation model, laboratory tests, case studies, and recommendations on control functionality which is of maximum benefit for the grid, within the limitations of wind turbine capability. The deliverable is referring to task 4.1.1. Measure of success: Quantified benefit to the grid of the strategies, and successful implementation of the functions in the baseline plant controller as well as successful verification in as well as real laboratory. Participant actions: DNV NL primary frequency response with rotor inertia, SINTEF virtual synchronous machine and reactive power control, laboratory verification DTU provide reactive power control tuning results of Task 4.2.1, VF contribute to development of methods for frequency and voltage support. ORE contribute to development of methods for frequency and voltage support.

Electro-mechanical model of reference wind power plant

Completed models of the Reference WPP in STAS and PSCAD/EMTP-RV. The deliverable is referring to task 1.2.4. Measure of success: STAS: calculation of reference plant modal frequencies and damping ratios, PSCAD/EMTP-RV: Compute voltage and current waveforms at the PCC. Participants actions: SINTEF is solely responsible.

Second project video

Each year a short video (24 min) will be produced presenting core aspects or achievements of the project. This is the second video. The deliverable is referring to task 5.1 and 5.2. Measure of success: 1 of 4 videos available to the public at the TotalControl website. Participants actions: DTU will work with professional videomakers to produce the video. Project partners will contribute to the content and take part in the videos as relevant.

Optimization of WPP set-points

Device set-points reflecting the optimal balance between WPP power production and cost of WPP loading from an economic perspective, and further to explore the sensitivity of model fidelity on resulting control schemes. The deliverable is referring to the overall task 2.2, incl. the subtasks 2.2.1, 2.2.2, 2.2.3, and 2.2.4. Measure of Success: Successful inclusion of the load aspect in development of optimized WPP control schemes. Participant actions: DTU is responsible for the medium fidelity and low fidelity models, KUL and DNV are responsible for the low fidelity models, and STATOIL is responsible for the load mitigation in various met-ocean conditions.

Flow database for reference wind farm

Collection of all flow simulation results for the reference WPP. The deliverable is referring to task 1.2.3. Measure of success: Complete data base covering detailed wind field and WPP operation parameters for different atmospheric conditions (e.g. stability classes) and transients, publically avialable. Participants actions: KUL, DTU, ORE will assemble the results data base. Hosted on the project website.

Predictive wind field model

Deliver a predictive model of wind velocity in the rotor plane (short term prediction ~10sec time scale) from LiDAR, and SCADA data, in combination with CFD modelling. The deliverable is referring to task Measure of success: Comparison of loads predicted by the estimator vs actual rotor loads. Participant actions: ORE is responsible for the analysis of measurements, modelling, and writing the report.

Reference Wind Power Plant

Document containing the technical specifications of the Reference WPP. The deliverable is referring to task 1.2.2. Measure of success: Complete Reference WPP description for the subsequent simulation and testing tasks. Participants actions: SINTEF main editor, contributions from all task participants.

Tower load reduction with LiDAR-assisted control

Adaptation and tuning of LiDAR-based control for 7MW turbine; simulation test results; controller software update for implementation on 7MW turbine; brief report. The deliverable is referring to task 3.1.5. Measure of success: Simulation results demonstrating effectiveness. Participant actions: DNVis responsible for the controller adjustments and software update, including writing the report.

Controller adaptation for varying conditions and ancillary services

Report on turbulence-based de-rating/uprating, parameter adaptation method, implementation of delta control and fast frequency response including controller-based and inverter-based methods and implications for turbine design; controller software update for implementation on 7MW turbine. The deliverable is referring to task 3.1.2. Measure of success: Demonstration of effectiveness of proposed enhancements using aeroelastic simulations. Participant actions: DNV is responsible for the controller development, simulation testing, implementation of controller changes, and writing of the report), SINTEF is responsible for the evaluation of VSM concept and effect of overpowering on turbine components. Besides that SINTEF will be contributing to the report.

Machine learning approaches to wind farm control

Report on the feasibility and applicability of machine learning approaches. the deliverable is referring to task 1.3.4. Measure of success: Detailed recommendations for applying the approach in practice. Participants actions: DNV is solely reesponsible.

Model predictive turbine control

Report on benefits and implementation issues with MPC; development of implementation suitable real-time application. The deliverable is referring to task 3.1.4. Measure of success: Simulation results demonstrating effectiveness and capability for real-time calculation. Participant actions: DNV will develope a real-time scheme for MPC implementation, and write the report.

Project master plan including full transparency of resources, schedule and cost/performance

The purpose is to give a brief description of the appropriate procedures, the templates and the reporting tools developed for TotalControl. The deliverable is referring to task 6.2. Measure of success: The report contains all relevant information. Participant actions: DTU will develop the procedures and the tools.

Hierarchical wind power plant supervisory controller

Documentation and source code for the baseline plant controller. The deliverable is referring to task 4.1.2. Measure of success: Controller is released as part of the TotalControl Toolbox and used by the consortium for comparative studies. Participant actions: SINTEF develop, verify, and document the controller.

Cost model for fatigue degradation and O&M

Development of cost models quantifying the cost of O&M and fatigue degradation of mechanical and electrical components. The deliverable is referring to task 2.1.1, 2.1.2, and 2.2.3. Measure of Success: Operational models that can be used to account for load effects in the development of cost optimized control schemes. Participant actions: DVN GL is responsible for the analysis of correlation between fatigue loading and O&M), DTU is responsible for the cost model for mechanical components; and SINTEF is responsible for the cost model for electrical components.

Title Reduction in OPEX based on maintaining target reliability levels through control

Quantification of cost of lowered annual reliability below design levels versus reduction in O&M cost for maintaining target reliability level. The deliverable is referring to task 2.4. Measure of Success: Prioritization of control methods that minimize O&M cost based on reliability margins. Participant actions: DTU is the only actor.

Tower load reduction using active damping

Report on possibilities for active damping to control tower loads for offshore turbines. The deliverable is referring to task 3.1.3. Measure of success: Simulation results demonstrating effectiveness. Participant actions: SINTEF is responsible for the damping strategy and tuning, simulations, and writing of the report.

Probabilistic framework to quantify the reliability levels of wind turbine structures under enhanced control methods

Statistical model setup that uses wind farm fatigue damage and power production models to predict annual reliability level and remaining lifetime of structural components of the turbine. The deliverable is referring to task 2.4. Measure of Success: Computationally fast quantification of reliability levels of turbine components as compared to target design level. Participant actions: DTU is the only actor.

SCADA-based conditions monitoring and fatigue estimation

Exploitation of the potential of conventional SCADA data for condition monitoring. The deliverable is referring to task 2.1.4. Measure of Success: Clarification of the potential of using SCADA data for WT condition monitoring. Participant actions: DNV is the only actor.

Setup of the website

Setup of the project website, including collaboration and communication tools among partners, repository for dissemination material, newsletters and social networks links. The deliverable is referring to task 5.2. Measure of success: a full operative website. Participant actions: DTU will design and maintain the website as well as newsletters and social media platforms to ensure that fully updated project information will be available online.

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Publications

Reduced-Order-VSM-Based Frequency Controller for Wind Turbines

Author(s): Liang Lu, Oscar Saborío-Romano, Nicolaos A. Cutululis
Published in: Energies, Issue 14/3, 2021, Page(s) 528, ISSN 1996-1073
DOI: 10.3390/en14030528

T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case

Author(s): Gregor Giebel; Christos Galinos; Jonas Kazda; Wai Hou Lio
Published in: Energies, Issue Volume 13, number 6, article number 1306, 2020, Page(s) 16 pages, ISSN 1996-1073
DOI: 10.3390/en13061306

Lidar Scanning of Induction Zone Wind Fields over Sloping Terrain

Author(s): T. Mikkelsen, M. Sjöholm, P. Astrup, A. Peña, G. Larsen, M. F. van Dooren, A. P. Kidambi Sekar
Published in: Journal of Physics: Conference Series, Issue 1452, 2020, Page(s) 012081, ISSN 1742-6588
DOI: 10.1088/1742-6596/1452/1/012081

Kalman-based interacting multiple-model wind speed estimator for wind turbines

Author(s): Wai Hou Lio, Fanzhong Meng
Published in: IFAC-PapersOnLine, Issue 53/2, 2020, Page(s) 12644-12649, ISSN 2405-8963
DOI: 10.1016/j.ifacol.2020.12.1840

Yaw induced wake deflection-a full-scale validation study

Author(s): G.C. Larsen, S. Ott, J. Liew, M.P. van der Laan, E. Simon, G.R. Thorsen, P. Jacobs
Published in: Journal of Physics: Conference Series, Issue 1618, 2020, Page(s) 062047, ISSN 1742-6588
DOI: 10.1088/1742-6596/1618/6/062047

Optimal open loop wind farm control

Author(s): J.A. Vitulli, G.C. Larsen, M.M. Pedersen, S. Ott, M. Friis-Møller
Published in: Journal of Physics: Conference Series, Issue 1256, 2019, Page(s) 012027, ISSN 1742-6588
DOI: 10.1088/1742-6596/1256/1/012027

Launch of the FarmConners Wind Farm Control benchmark for code comparison

Author(s): Irene Eguinoa; Konstanze Kölle; Filippo Campagnolo; Mikel Iribas-Latour; Johan Meyers; Tuhfe Göçmen; Thomas Duc; David Astrain; Jan-Willem van Wingerden; Carlo L. Bottasso; Søren Juhl Andersen; Gregor Giebel
Published in: Journal of Physics: Conference Series, Issue Vol. 1618, issue number 2, 2020, Page(s) 10 pagers, ISSN 1742-6596
DOI: 10.1088/1742-6596/1618/2/022040

Dynamic wake tracking and characteristics estimation using a cost-effective LiDAR

Author(s): Wai Hou Lio, Gunner C. Larsen, Niels K. Poulsen
Published in: Journal of Physics: Conference Series, Issue 1618, 2020, Page(s) 032036, ISSN 1742-6588
DOI: 10.1088/1742-6596/1618/3/032036

A Minimalistic Prediction Model to Determine Energy Production and Costs of Offshore Wind Farms

Author(s): Jens Nørkær Sørensen, Gunner Christian Larsen
Published in: Energies, Issue 14/2, 2021, Page(s) 448, ISSN 1996-1073
DOI: 10.3390/en14020448

Optimal dynamic induction and yaw control of wind farms: effects of turbine spacing and layout

Author(s): Wim Munters, Johan Meyers
Published in: Journal of Physics: Conference Series, Issue 1037, 2018, Page(s) 032015, ISSN 1742-6588
DOI: 10.1088/1742-6596/1037/3/032015

Improved modelling of fatigue loads in wind farms under non-neutral ABL stability conditions

Author(s): G.C. Larsen, S. Ott, T.J. Larsen, K.S. Hansen, A. Chougule
Published in: Journal of Physics: Conference Series, Issue 1037, 2018, Page(s) 072013, ISSN 1742-6588
DOI: 10.1088/1742-6596/1037/7/072013

Combining induction control and wake steering for wind farm energy and fatigue loads optimisation

Author(s): Ervin Bossanyi
Published in: Journal of Physics: Conference Series, Issue 1037, 2018, Page(s) 032011, ISSN 1742-6588
DOI: 10.1088/1742-6596/1037/3/032011

Integrated wind farm layout and control optimization

Author(s): Gunner Chr. Larsen; Mads Mølgaard Pedersen
Published in: Wind Energy Science, Vol 5, Pp 1551-1566 (2020), Issue 1, 2020, ISSN 2366-7443
DOI: 10.5194/wes-5-1551-2020

Virtual synchronous machine control for wind turbines: a review

Author(s): L Lu, N A Cutululis
Published in: Journal of Physics: Conference Series, Issue 1356, 2019, Page(s) 012028, ISSN 1742-6588
DOI: 10.1088/1742-6596/1356/1/012028

Dynamic wake tracking using a cost-effective LiDAR and Kalman filtering: Design, simulation and full-scale validation

Author(s): Wai Hou Lio, Gunner Chr. Larsen, Gunhild R. Thorsen
Published in: Renewable Energy, Issue 172, 2021, Page(s) 1073-1086, ISSN 0960-1481
DOI: 10.1016/j.renene.2021.03.081

Effective wind speed estimation for wind turbines in down-regulation

Author(s): Alan Wai Hou Lio, Fanzhong Meng
Published in: Journal of Physics: Conference Series, Issue 1452, 2020, Page(s) 012008, ISSN 1742-6588
DOI: 10.1088/1742-6596/1452/1/012008

Effect of conventionally neutral boundary layer height on turbine performance and wake mixing in offshore windfarms

Author(s): Ishaan Sood, Wim Munters, Johan Meyers
Published in: Journal of Physics: Conference Series, Issue 1618, 2020, Page(s) 062049, ISSN 1742-6588
DOI: 10.1088/1742-6596/1618/6/062049

The effect of minimum thrust coefficient control strategy on power output and loads of a wind farm

Author(s): Fanzhong Meng, Alan Wai Hou Lio, Jaime Liew
Published in: Journal of Physics: Conference Series, Issue 1452, 2020, Page(s) 012009, ISSN 1742-6588
DOI: 10.1088/1742-6596/1452/1/012009

Model-free estimation of available power using deep learning

Author(s): Tuhfe Göçmen, Albert Meseguer Urbán, Jaime Liew, Alan Wai Hou Lio
Published in: Wind Energy Science, Issue 6/1, 2021, Page(s) 111-129, ISSN 2366-7451
DOI: 10.5194/wes-6-111-2021

A virtual Synchronous Machine control Scheme for Wind Turbines

Author(s): Liang Lu; Nicolaos A. Cutululis
Published in: 2019

Grid frequency stability with wind power: Irish case study using a new closed loop simulation environment

Author(s): Wouter Schoot, Wouter de Boer, Ervin Bossanyi
Published in: 2020

TotalControl - Advanced integrated control of large-scale wind power plants and wind turbines

Author(s): Giebel, G; G. Larsen; A. Natarajan; J. Meyers; E. Bossanyi and K. Merz
Published in: WindEurope 2019 Conference Proceedings, 2019

A data-driven flow model for wind-farm control based on Koopman mode decomposition of large-eddy simulations

Author(s): Munters, W. and J. Meyers
Published in: 2018

Enhanced Frequency Control Capability from Wind Turbine Generators and Wind Power Plants

Author(s): Liang Lu
Published in: 2018

Virtual Synchronous Machine Control for Wind Turbines: A Review

Author(s): L. Lu and N. A. Cutululis
Published in: 2019