In-operation learning of optimal wind farm operation strategy
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Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/112344
Tipus de documentProjecte Final de Màster Oficial
Data2017-09
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
In a wind farm, power losses due to wind turbine wake effects can be up to 30-40% under
certain conditions. As the global installed wind power capacity increases, the mitigation
of wake effects in wind farms is gaining more importance. Following a conventional
control strategy, each individual turbine maximizes its own power production without
taking into consideration its effects on the performance of downstream turbines.
Therefore, this control scheme results in operation conditions that yield suboptimal power
production.
In order to increase the overall wind farm power production, a cooperative control
strategy can be used, which coordinates the control actions among the wind turbines in
the wind farm. This work further investigates the model-free Bayesian Ascent
optimization algorithm using SimWindFarm and a standalone Dynamic Wake
Meandering model-based simulation tool
An advantage of such optimization approach is that the control strategy adapts to
operational conditions in the wind farm and is not model-dependent. An approximation
of the wind farm power function is constructed using GP regression to fit the control
action inputs and the noisy measured power outputs, which is then maximized to
determine the optimal control inputs. This estimation is updated in every iteration,
allowing the control system to learn from the target system while performing the
optimization. The usage of all historical data, along with a trust region constraint in the
sampling of new inputs, contribute to a fast convergence rate with gradual changes of the
control actions.
The developed learning technique is implemented in a wind farm controller and tested in
both SimWindFarm and standalone Dynamic Wake Meandering model-based simulation
tools. With the conducted tests, performance of the algorithm is assessed considering the different dynamics in the wind farm, thus obtaining an accurate representation of real
farm operation. The developed controller reliably improves farm efficiency, even with
uncertainty present in measurements.
Compared to traditional control strategies, an increase in total wind farm power
production is obtained when using a cooperative control strategy. Such enhancement in
wind farm performance would result in an improvement of wind farm economics and
hence in further growth of wind-energy based power generation.
TitulacióMÀSTER UNIVERSITARI EN ENGINYERIA INDUSTRIAL (Pla 2014)
Col·leccions
Fitxers | Descripció | Mida | Format | Visualitza |
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MasterThesisReport_JoanOliva.pdf | Memòria | 3,268Mb | Visualitza/Obre |