09:30 - 11:00 Science & research - Controlling offshore
In this session you will learn about controls of offshore wind turbines. The topics ranges from floating platforms to load mitigation.
- Summerize the latest control trends
- Explain control of floating platforms
- Predict how controls can mitigate loads
- Explain how control strategies impact cost of energy
Lead Session Chair:
Thomas Buhl, Head of Section: Wind Turbines & Responsible for Offshore, DTU Wind Energy, Denmark
Ervin Bossanyi, DNV - GL
Torben Knudsen (1) F P Rafael Wisniewski (1) Thomas Bak (1)
(1) Aalborg University, Aalborg East, Denmark
Printer friendly version: Print
Presenter's biographyBiographies are supplied directly by presenters at OFFSHORE 2015 and are published here unedited
Dr. Odgaard has since April 2013 been an associate professor at Aalborg University in Denmark. From 2007 to April 2013 he was a R&D Engineer and Control Specialist at kk-electronic. His research interests are within the field of advance wind turbine control. More specifically model predictive control and fault diagnosis and fault tolerant control of wind turbines. He is the main proposer of several benchmark models for fault tolerant control of wind turbines and wind farms. Dr. Odgaard is a IEEE Senior Member and a member of IFAC Technical Committees of Safeprocess and Power and Energy Systems.
Optimized control strategy for overloaded offshore wind turbines
Operation and maintenance cost are an important part of cost of energy especially for offshore wind farms. Typically unplanned service is called for due to detection off excessive loads on components, e.g., the tower.
In the process of decreasing the cost of energy of wind turbines it is relevant to continue some level of production while awaiting service and repair of the offshore wind turbine. This is typically done operating the wind turbine in a power de-rated operation mode, assuming that lower power generation results in lower loads on the wind turbine, which enables continued production until next service.
Recent results in Model Predictive Control (MPC) applied to wind turbines show a potential for presenting possible controller tunings which weighs multiple conflicting objectives of generated power and the damage equivalent loads for tower fore-aft. Based on these different weights Pareto Fronts depicting the relationship between the generated power and the damage equivalent loads can be computed. Results have shown that the Pareto Front is a very effective tool for selecting the best controller tuning for a given wind turbine. It also enables a very safe and robust comparison between a new control strategy and the present one.
Main body of abstract
Is it true that power de-rating indeed the best way to reduce loads?
The power de-rating approach has the drawback of only indirectly handling the changed objective of the wind turbine operation while awaiting the repair, instead of dealing directly with relevant fatigue loads on the wind turbine.
This paper describes and demonstrates this new controller selection tool for the case of operating wind turbine with faults, awaiting repair and service at offshore location, where accessibility can be problematic. The controller objectives are focused directly on the actual objective like lowering of fore aft fatigue loads, instead of using an indirect objective of de-rating the power production of the wind turbine.
This means what the wind turbine controller can be selected and tuned directly to obtain a specified reduced fatigue load/ DEL while awaiting service. This will in most cases increase the power generation, compared to the de-rating approach. The latter is a conservative approach, which might end up with too high power reduction as results of ensuring the load reduction to the given level.
A MPC controller is configured and tuned using this novel approach including Pareto fronts. It is compared to the existing de-rating strategy using high fidelity aero-servo-elastic simulation code and the possibilities for reduction of cost of energy are outlined.
In this paper computed Pareto Fronts of a Model Predictive Controller of a wind turbine are used to de-tune a wind turbine operating with faults, such that a controller tuning is selected which lowers the fatigue loads of the wind turbine, such that the model predictive controller is selected for a given fatigue load objective. The Pareto Fronts and evaluations of the proposed scheme is evaluated in a high fidelity aero-eleastic code.
This work has two main learning objectives.
Compute Pareto Fronts of generated power and tower fore-aft fatigue loads of a wind turbine for a number a model predictive controller turnings with different weightings between power generation and load reduction.
Selection of model predictive controller turnings depending on the operational conditions of the wind turbine. e.g. for offshore wind turbines with faults.