A new method for prediction of power coefficient and wake length of a horizontal axis wind turbine based on energy analysis

Wang, Yan; Wang, Liang; Jiang, Yao; Sun, Xiaojing

doi:10.1016/j.enconman.2021.115121

Cited by 15 publications

(1 citation statement)

References 51 publications

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“…Wind farms face a challenge regarding yaw systematic error due to wake interaction between the wind turbines situated in a wind farm, which causes high extreme loads on the downstream rows and affects the power coefficient. The estimation of the WT system's power coefficient and the wake length has great role in the wind energy conversion system and wind farm design [145,146]. Large eddy simulation (LES) combined with actuator line method (ALM) were used to simulate the flow field in the wind farm and around each turbine, then the proper orthogonal decomposition (POD) method was used to analyze the energy in each flow plane.…”

Section: Yaw Control Techniquementioning

confidence: 99%

Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review

Elkodama,

Ismaiel,

Abdellatif

et al. 2023

Energies

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In recent years, the increasing environmental problems, especially the issue of global warming, have motivated demand for a cleaner, more sustainable, and economically viable energy source. In this context, wind energy plays a significant role due to the small negative impact it has on the environment, which makes it among the most widespread potential sustainable renewable fuel nowadays. However, wind turbine control systems are important factors in determining the efficiency and cost-effectiveness of a wind turbine (WT) system for wind applications. As wind turbines become more flexible and larger, it is difficult to develop a control algorithm that guarantees both efficiency and reliability as these are conflicting objectives. This paper reviews various control strategies for the three main control systems of WT, which are pitch, torque, and yaw control, in different operational regions considering multi-objective control techniques. The different control algorithms are generally categorized as classical, modern (soft computing) and artificial intelligence (AI) for each WT control system. Modern and soft computing techniques have been showing remarkable improvement in system performance with minimal cost and faster response. For pitch and yaw systems, soft computing control algorithms like fuzzy logic control (FLC), sliding mode control (SMC), and maximum power point tracking (MPPT) showed superior performance and enhanced the WT power performance by up to 5% for small-scale WTs and up to 2% for multi-megawatt WTs. For torque control systems, direct torque control (DTC) and MPPT AI-based techniques were suitable for reducing generator torque fluctuations and estimating the torque coefficient for different wind speed regions. Classical control techniques such as PI/PID resulted in poor dynamic response for large-scale WTs. However, to improve classical control techniques, AI algorithms could be used to tune the controller’s parameters to enhance its response, as a WT is a highly non-linear system. A graphical abstract is presented at the end of the paper showing the pros/cons of each control system category regarding each WT control system.

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Section: Yaw Control Techniquementioning

confidence: 99%