Adaptive Inverse Deep Reinforcement Lyapunov learning control for a floating wind turbine

KhalafAnsar, Hadi Mohammadian; Keighobadi, Jafar

doi:10.24200/sci.2023.61871.7532

Cited by 2 publications

(2 citation statements)

References 38 publications

(43 reference statements)

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“…Although extensive experiments were conducted to demonstrate the performance of SAABC-CS, we hope to theoretically analyze the algorithm, inspired by the literature [40][41][42]. We also wish to extend the use of SAABC-CS to certain large and expensive problems in the future.…”

Section: Discussionmentioning

confidence: 99%

Self-adaptive Artificial Bee Colony with a Candidate Strategy Pool

Huang,

Yu,

Guo

et al. 2023

Applied Sciences

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As a newly developed metaheuristic algorithm, the artificial bee colony (ABC) has garnered a lot of interest because of its strong exploration ability and easy implementation. However, its exploitation ability is poor and dramatically deteriorates for high-dimension and/or non-separable functions. To fix this defect, a self-adaptive ABC with a candidate strategy pool (SAABC-CS) is proposed. First, several search strategies with different features are assembled in the strategy pool. The top 10% of the bees make up the elite bee group. Then, we choose an appropriate strategy and implement this strategy for the present population according to the success rate learning information. Finally, we simultaneously implement some improved neighborhood search strategies in the scout bee phase. A total of 22 basic benchmark functions and the CEC2013 set of tests were employed to prove the usefulness of SAABC-CS. The impact of combining the five methods and the self-adaptive mechanism inside the SAABC-CS framework was examined in an experiment with 22 fundamental benchmark problems. In the CEC2013 set of tests, the comparison of SAABC-CS with a number of state-of-the-art algorithms showed that SAABC-CS outperformed these widely-used algorithms. Moreover, despite the increasing dimensions of CEC2013, SAABC-CS was robust and offered a higher solution quality.

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Section: Discussionmentioning

confidence: 99%

Self-adaptive Artificial Bee Colony with a Candidate Strategy Pool

Huang,

Yu,

Guo

et al. 2023

Applied Sciences

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“…Moreover, in [103], a novel control system based on DRL was introduced to mitigate the impact of both disturbance and noise effects in the context of FOWTs. The system leverages the large volume of information generated by significant variations in wind and water waves to facilitate the convergent learning of neural network models for the wind turbine.…”

Section: Data-driven Model-free Literature Overviewmentioning

confidence: 99%

A Comprehensive Review on Advanced Control Methods for Floating Offshore Wind Turbine Systems above the Rated Wind Speed

Didier,

Liu,

Laghrouche

et al. 2024

Energies

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This paper presents a comprehensive review of advanced control methods specifically designed for floating offshore wind turbines (FOWTs) above the rated wind speed. Focusing on primary control objectives, including power regulation at rated values, platform pitch mitigation, and structural load reduction, this paper begins by outlining the requirements and challenges inherent in FOWT control systems. It delves into the fundamental aspects of the FOWT system control framework, thereby highlighting challenges, control objectives, and conventional methods derived from bottom-fixed wind turbines. Our review then categorizes advanced control methods above the rated wind speed into three distinct approaches: model-based control, data-driven model-based control, and data-driven model-free control. Each approach is examined in terms of its specific strengths and weaknesses in practical application. The insights provided in this review contribute to a deeper understanding of the dynamic landscape of control strategies for FOWTs, thus offering guidance for researchers and practitioners in the field.

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Adaptive Inverse Deep Reinforcement Lyapunov learning control for a floating wind turbine

Cited by 2 publications

References 38 publications

Self-adaptive Artificial Bee Colony with a Candidate Strategy Pool

Self-adaptive Artificial Bee Colony with a Candidate Strategy Pool

A Comprehensive Review on Advanced Control Methods for Floating Offshore Wind Turbine Systems above the Rated Wind Speed

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