Application of fuzzy logic method in wind turbine yaw control system to obtain maximum energy: a methodological and prototype approach

Bharani, R.; Sivaprakasam, A.

doi:10.1007/s00202-021-01396-1

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Table 1 summarizes the reviewed papers and references the number of each control type for each WT control system. [90][91][92][93][94][95][96][97][98][99][102][103][104][105][106][107][108][109][110][111][112][113]115,120,[122][123][124][125]127,147,148,150] AI [100,101,114,[116][117][118][119]121,126,[128][129][130][131][132][133][134]…”

Section: Discussion and Main Trendsmentioning

confidence: 99%

“…In ref. [118], Ramaswamy Bharani et al designed a simple, low cost, and accurate yaw positioning fuzzy logic controller equipped with a wind turbine sensor and an angle position sensor on the tower top in order to track the maximum wind velocity. The accuracy of the controller of ±1.5 • has been validated using a real-time dataset.…”

Section: Yaw Control Techniquementioning

confidence: 99%

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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: Discussion and Main Trendsmentioning

confidence: 99%

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 (4), when adjusting the yaw dead zone according to the correlation analysis, it is necessary to consider avoiding excessive power loss. Considering the actual situation of industrial applications and similar designs of related studies [27][28][29][30], four different dead zone settings will be used in this study, which are ±5 • , ±15 • , ±30 • , and ±45 • . When performing step (4.2), the selection of 30 • and 45 • will further depend on the proportion of power loss under actual working conditions.…”

Section: Expert System For Yaw Controlmentioning

confidence: 99%

Anti‐tropical cyclone yaw control of wind turbines based on knowledge learning and expert system

Cai¹,

Feng²,

Yao

et al. 2023

IET Control Theory & Appl

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A tropical cyclone (TC) is extreme weather severely threatening coastal wind turbines. However, the non‐extreme wind speed period (nEWSP) before and after it can also bring considerable economic benefits to wind farms. Aiming at the working conditions of nEWSP‐TC, this paper analyzes the characteristics of the wind field based on historical actual TC data and constructs a pseudo‐Monte Carlo experiment, uses the experimental results to construct a knowledge base and an inference engine, and forms an expert system to guide the yaw control of wind turbines in nEWSP‐TC. The simulation results show that the yaw error has different effects on the fatigue load of the wind turbine under different working conditions of nEWSP‐TC, and the proposed improved yaw strategy can reduce the fatigue load of the wind turbine under the premise of limited power loss and improve safe operation capability of wind turbines under nEWSP‐TC conditions.

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A Statistic-Based Static Error Warning Method for Wind Turbine Yaw-Control System

Li,

Zhang

2024

2024 4th International Conference on Energy Engineering and Power Systems (EEPS)

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Application of fuzzy logic method in wind turbine yaw control system to obtain maximum energy: a methodological and prototype approach

Cited by 5 publications

References 14 publications

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

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

Anti‐tropical cyclone yaw control of wind turbines based on knowledge learning and expert system

A Statistic-Based Static Error Warning Method for Wind Turbine Yaw-Control System

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