FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2914211392> ?p ?o ?g. }

• W2914211392 endingPage "436" @default.
• W2914211392 startingPage "436" @default.
• W2914211392 abstract "This paper introduces a novel data driven yaw control algorithm synthesis method based on Reinforcement Learning (RL) for a variable pitch variable speed wind turbine. Yaw control has not been extendedly studied in the literature; in fact, most of the currently considered developments in the scope of the wind energy are oriented to the pitch and speed control. The most important drawbacks of the yaw control are the very large time constants and the strict yaw angle change rate constraints due to the high mechanical loads when the wind turbine angle is changed in order to adequate it to the wind speed orientation. An optimal yaw control algorithm needs to be designed in order to adapt the rotor orientation depending on the wind turbine dynamics and the local wind speed regime. Consequently, the biggest challenge of the yaw control algorithm is to decide the moment and the quantity of the wind turbine orientation variation to achieve the highest quantity of power at each instant, taking into account the constraints derived from the mechanical limitations of the yawing system and the mechanical loads. In this paper, a novel based algorithm based on the RL Q-Learning algorithm is introduced. The first step is to obtain a model of the power generated by the wind turbine (a real onshore wind turbine in this paper) through a power curve, that in conjunction with a conventional proportional regulator will be used to obtain a dataset that explains the actual behaviour of the real wind turbine when a variety of different yaw control commands are imposed. That knowledge is then used to learn the best control action for each different state of the wind turbine with respect to the wind direction represented by the yaw angle, storing that knowledge in a matrix Q(s,a). The last step is to model that matrix through a MultiLayer Perceptron with BackPropagation (MLP-BP) Artificial Neural Network (ANN) to avoid large matrix management and quantification problems. Once that the optimal yaw controller has been synthetized, its performance has been assessed using a number of wind speed realizations obtained using the software application TurbSim, in order to analyze how the introduced novel algorithm deals with different wind speed scenarios." @default.
• W2914211392 created "2019-02-21" @default.
• W2914211392 creator A5041257635 @default.
• W2914211392 creator A5043444298 @default.
• W2914211392 creator A5047153625 @default.
• W2914211392 creator A5059450757 @default.
• W2914211392 creator A5063298294 @default.
• W2914211392 date "2019-01-30" @default.
• W2914211392 modified "2023-09-30" @default.
• W2914211392 title "Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control" @default.
• W2914211392 cites W1982262386 @default.
• W2914211392 cites W1987725948 @default.
• W2914211392 cites W1992689299 @default.
• W2914211392 cites W2016798398 @default.
• W2914211392 cites W2026557256 @default.
• W2914211392 cites W2119550060 @default.
• W2914211392 cites W2125122159 @default.
• W2914211392 cites W2153846939 @default.
• W2914211392 cites W2161600214 @default.
• W2914211392 cites W2336221921 @default.
• W2914211392 cites W2414980438 @default.
• W2914211392 cites W2415664089 @default.
• W2914211392 cites W2518255611 @default.
• W2914211392 cites W2594620841 @default.
• W2914211392 cites W2596114269 @default.
• W2914211392 cites W2620037712 @default.
• W2914211392 cites W2754703339 @default.
• W2914211392 cites W2776624591 @default.
• W2914211392 cites W2783247157 @default.
• W2914211392 cites W2783539755 @default.
• W2914211392 cites W2783831967 @default.
• W2914211392 cites W2799615857 @default.
• W2914211392 cites W2799908433 @default.
• W2914211392 cites W2802352055 @default.
• W2914211392 cites W2885534617 @default.
• W2914211392 cites W2886246186 @default.
• W2914211392 cites W2896079223 @default.
• W2914211392 cites W2897279355 @default.
• W2914211392 cites W32403112 @default.
• W2914211392 doi "https://doi.org/10.3390/en12030436" @default.
• W2914211392 hasPublicationYear "2019" @default.
• W2914211392 type Work @default.
• W2914211392 sameAs 2914211392 @default.
• W2914211392 citedByCount "61" @default.
• W2914211392 countsByYear W29142113922019 @default.
• W2914211392 countsByYear W29142113922020 @default.
• W2914211392 countsByYear W29142113922021 @default.
• W2914211392 countsByYear W29142113922022 @default.
• W2914211392 countsByYear W29142113922023 @default.
• W2914211392 crossrefType "journal-article" @default.
• W2914211392 hasAuthorship W2914211392A5041257635 @default.
• W2914211392 hasAuthorship W2914211392A5043444298 @default.
• W2914211392 hasAuthorship W2914211392A5047153625 @default.
• W2914211392 hasAuthorship W2914211392A5059450757 @default.
• W2914211392 hasAuthorship W2914211392A5063298294 @default.
• W2914211392 hasBestOaLocation W29142113921 @default.
• W2914211392 hasConcept C119599485 @default.
• W2914211392 hasConcept C121332964 @default.
• W2914211392 hasConcept C127413603 @default.
• W2914211392 hasConcept C133731056 @default.
• W2914211392 hasConcept C146160929 @default.
• W2914211392 hasConcept C146978453 @default.
• W2914211392 hasConcept C153294291 @default.
• W2914211392 hasConcept C154945302 @default.
• W2914211392 hasConcept C161067210 @default.
• W2914211392 hasConcept C16345878 @default.
• W2914211392 hasConcept C16389437 @default.
• W2914211392 hasConcept C171146098 @default.
• W2914211392 hasConcept C201320609 @default.
• W2914211392 hasConcept C206831581 @default.
• W2914211392 hasConcept C2524010 @default.
• W2914211392 hasConcept C2775924081 @default.
• W2914211392 hasConcept C2776336204 @default.
• W2914211392 hasConcept C2778330180 @default.
• W2914211392 hasConcept C2778449969 @default.
• W2914211392 hasConcept C33923547 @default.
• W2914211392 hasConcept C41008148 @default.
• W2914211392 hasConcept C47446073 @default.
• W2914211392 hasConcept C50644808 @default.
• W2914211392 hasConcept C78519656 @default.
• W2914211392 hasConcept C78600449 @default.
• W2914211392 hasConceptScore W2914211392C119599485 @default.
• W2914211392 hasConceptScore W2914211392C121332964 @default.
• W2914211392 hasConceptScore W2914211392C127413603 @default.
• W2914211392 hasConceptScore W2914211392C133731056 @default.
• W2914211392 hasConceptScore W2914211392C146160929 @default.
• W2914211392 hasConceptScore W2914211392C146978453 @default.
• W2914211392 hasConceptScore W2914211392C153294291 @default.
• W2914211392 hasConceptScore W2914211392C154945302 @default.
• W2914211392 hasConceptScore W2914211392C161067210 @default.
• W2914211392 hasConceptScore W2914211392C16345878 @default.
• W2914211392 hasConceptScore W2914211392C16389437 @default.
• W2914211392 hasConceptScore W2914211392C171146098 @default.
• W2914211392 hasConceptScore W2914211392C201320609 @default.
• W2914211392 hasConceptScore W2914211392C206831581 @default.
• W2914211392 hasConceptScore W2914211392C2524010 @default.
• W2914211392 hasConceptScore W2914211392C2775924081 @default.
• W2914211392 hasConceptScore W2914211392C2776336204 @default.

• next