Wind Direction Prediction Based on Nonlinear Autoregression and Elman Neural Networks for the Wind Turbine Yaw System

Yang, Yusong; Solomin, Evgeny; Shishkov, Aleksandr

doi:10.2139/ssrn.4128942

Cited by 1 publication

(1 citation statement)

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“…Yanga et al proposed a wind direction prediction algorithm based on non-linear autoregression (NAR) combined with an Elman neural network (ENN) in order to reduce the wind direction determination error as it includes a feedback property hidden from the input layer [143]. The NAR algorithm represents a good nonlinear mapping technique that allows computing 'n' times of nodes before and after the present value and has an excellent ability of dynamic tracking and rich state recording of information.…”

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%