Data‐driven robust value iteration control with application to wind turbine pitch control

Liu, Yang; Jiang, Zhanpeng; Hao, Lichao; Xing, Zuoxia; Chen, Mingyang; Zhang, Pengfei

doi:10.1002/oca.2834

Cited by 10 publications

(7 citation statements)

References 38 publications

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“…Similar controls-oriented efforts can be seen in work by Yang Liu et al [53], whom also designed a data-driven pitch controller to reduce computational expense of the system model, as well as in work by Jingjie Xie et al [54], who use a NN-based adaptive controls algorithm to manage pitch and torque control. The efforts mentioned in [52][53][54] take advantage of the reduction in computational expense that data-driven methods provide, as does the work by M. Nouri Manzar and A. Khaki-Sedigh [55]. Their effort, too, involves the use of a data-driven model to enable active control of pitch and torque control schemes, through reduction of computational expense for the controller to operate in real-time.…”

Section: Performance Monitoringmentioning

confidence: 80%

“…Performance monitoring is another ongoing avenue of research for data-driven modeling in the wind turbine space [31,[49][50][51][52][53][54][55][56][57][58][59][60][61][62][63]. For the purposes of this paper, performance modelling is classified here as any task where data-driven models are utilized to observe, predict, control, and optimize the performance of individual turbines, or turbine farms, referencing tasks as such as in [31].…”

Section: Performance Monitoringmentioning

confidence: 99%

See 1 more Smart Citation

Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads

Roetzer,

Li,

Hall

2024

Preprint

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With the increasing use of data-driven modeling methods new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. Literature shows the advantages of data-driven methods reduce computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies’ aptitude for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation data sets, with Gaussian Process Regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine.

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Section: Performance Monitoringmentioning

confidence: 80%

Section: Performance Monitoringmentioning

confidence: 99%

Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads

Roetzer,

Li,

Hall

2024

Preprint

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“…However, most of the aforementioned references only consider economic and carbon emission costs, without considering issues such as comprehensive energy utilization and self-sufficiency [5,6]. This study aims to address this urgent need and propose an innovative method to optimize the configuration of park level MECS, which considers multiple evaluation indicators.…”

Section: Research On Optimal Configuration Of Park-level Multi-energy...mentioning

confidence: 99%

Research on optimal configuration of park-level multi-energy complementary system with multiple evaluation indexes

Wu¹,

Chen²,

Zhang³

et al. 2023

Preprint

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At present, the shortage of energy is becoming more and more serious, and the ecological environment is deteriorating. The proposal of the concept of park level multi energy complementary system (MECS) provides direction for achieving environmentally friendly and sustainable energy development. In recent years, how to set the capacity and scheduling methods of equipment to improve the economy and reliability of the system has become a hot research topic in this field. In this paper, a two-layer optimal scheduling strategy is proposed to allocate the capacity of various energy equipment in the park, considering the comprehensive energy self-sufficiency rate, comprehensive energy utilization rate and energy shortage expectation. The proposed capacity allocation scheme can effectively improve the economy of MECS in the park. Finally, the effectiveness and practicability of the algorithm are verified by simulation analysis.

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“…In recent years, the ADP (Approximate Dynamic Programming) algorithm has gained significant attention in academia and has been rapidly adopted in industry [ 12 , 13 , 14 , 15 , 16 , 17 ]. The remarkable control performance of the ADP algorithm in traditional control applications has demonstrated its capability to handle complex control tasks, attracting numerous researchers to study its theories and applications.…”

Section: Introductionmentioning

confidence: 99%

Tracking Control for a Lower Extremity Exoskeleton Based on Adaptive Dynamic Programing

Su,

Pei,

Tang

2023

Biomimetics

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The utilization of lower extremity exoskeletons has witnessed a growing presence across diverse domains such as the military, medical treatment, and rehabilitation. This paper introduces a novel design of a lower extremity exoskeleton specifically tailored for individuals engaged in heavy object carrying tasks. The exoskeleton incorporates an impressive 12 degrees of freedom (DOF), with four of them being effectively controlled through hydraulic cylinders. To achieve optimal control of this intricate lower extremity exoskeleton system, the authors propose an adaptive dynamic programming (ADP) algorithm. Several crucial components are established to implement this control scheme. These include the formulation of the state equation for the lower extremity exoskeleton system, which is well-suited for the ADP algorithm. Additionally, a corresponding performance index function based on the tracking error is devised, along with the game algebraic Riccati equation. By employing the value iteration ADP scheme, the lower extremity exoskeleton demonstrates highly effective tracking control. This research not only highlights the potential of the proposed control approach but also showcases its ability to enhance the overall performance and functionality of lower extremity exoskeletons, particularly in scenarios involving heavy object carrying. Overall, this study contributes to the advancement of lower extremity exoskeleton technology and offers valuable insights into the application of ADP algorithms for achieving precise and efficient control in demanding tasks.

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Data‐driven robust value iteration control with application to wind turbine pitch control

Cited by 10 publications

References 38 publications

Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads

Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads

Research on optimal configuration of park-level multi-energy complementary system with multiple evaluation indexes

Tracking Control for a Lower Extremity Exoskeleton Based on Adaptive Dynamic Programing

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