Yaw Control of Wind Turbine Using Fuzzy Logic Controller

Bharani, R.; Jayasankar, K. C.

doi:10.1007/978-81-322-2119-7_97

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Active control systems for small wind turbines regulated the rotational speed and kept the power constant in the rated region. Notably, the YAW and PITCH control systems were related to the electrical production efficiency [15]; the YAW systems regulated the power in regions higher than nominal speeds, using the On/Off technique, and proposed an orientation speed of 3°/s as the most appropriate to limit the power [16]. On the other hand, YAW systems presented an error defined by the variation between the wind direction and the nacelle position, which caused the generated power to decrease [17].…”

Section: Regulating Systemsmentioning

confidence: 99%

Development of Active Wind Vane for Low-Power Wind Turbines

González Domínguez,

Lastres Danguillecourt,

Verde Añorve

et al. 2024

Energies

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This paper proposes the development of an active control system to control the power output of a low-power horizontal-axis wind turbine (HAWT) when operating at wind speeds above the rated wind speed. The system is composed of an active articulated vane (AAV) in charge of the orientation of the wind turbine, which is driven by an electric actuator that changes the angle of the AAV to maintain a constant power output. Compared with the passive power regulation systems most often used in low-power HAWTs, active systems allow for better control and, therefore, greater stability of the delivered power, which reduces the structural stresses and allows for controlled braking in any wind condition or during system failures. The control system was designed and simulated using MATLAB R2022b software, and then built and evaluated under laboratory conditions. For the control design, the transfer function (TF) between the pulse width modulation (PWM) and the AAV angle (θ) was determined via laboratory tests using MATLAB’s PIDTurner tool. For the simulation, the relationship between the power output and the AAV angle was determined using the vector decomposition of the wind speed and wind rotor area. Wind speed step and ramp response tests were performed for proportional–integral–derivative (PID) control. The results obtained demonstrate the technical feasibility of this type of control, obtaining settling times (ts) of 6.7 s in the step response and 2.8 s in the ramp response.

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Section: Regulating Systemsmentioning

confidence: 99%

Development of Active Wind Vane for Low-Power Wind Turbines

González Domínguez,

Lastres Danguillecourt,

Verde Añorve

et al. 2024

Energies

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show abstract

“…Yaw control of a wind turbine can be achieved via diverse control strategies. Despite of the fact that some advanced techniques such as Fuzzy Logic Control presented by Bharani et al [33] and Model Predictive Control presented by Song et al [34] are object of study nowadays, classical Proportional Integral Derivative (PID) based strategies [15,16] can still be found in recent literature.…”

Section: Theoretical Background Of the Conventional Yaw Control Strategymentioning

confidence: 99%

Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control

et al. 2019

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The yaw angle control of a wind turbine allows maximization of the power absorbed from the wind and, thus, the increment of the system efficiency. Conventionally, classical control algorithms have been used for the yaw angle control of wind turbines. Nevertheless, in recent years, advanced control strategies have been designed and implemented for this purpose. These advanced control strategies are considered to offer improved features in comparison to classical algorithms. In this paper, an advanced yaw control strategy based on reinforcement learning (RL) is designed and verified in simulation environment. The proposed RL algorithm considers multivariable states and actions, as well as the mechanical loads due to the yaw rotation of the wind turbine nacelle and rotor. Furthermore, a particle swarm optimization (PSO) and Pareto optimal front (PoF)‐based algorithm have been developed in order to find the optimal actions that satisfy the compromise between the power gain and the mechanical loads due to the yaw rotation. Maximizing the power generation and minimizing the mechanical loads in the yaw bearings in an automatic way are the objectives of the proposed RL algorithm. The data of the matrices Q (s,a) of the RL algorithm are stored as continuous functions in an artificial neural network (ANN) avoiding any quantification problem. The NREL 5‐MW reference wind turbine has been considered for the analysis, and real wind data from Salt Lake, Utah, have been used for the validation of the designed yaw control strategy via simulations with the aeroelastic code FAST.

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“…Fuzzy logic controller is used in tracking the direction of maximum wind speed. The direction of maximum wind speed is sensed, it used as reference and wind turbine head is moved towards the direction of maximum wind speed to get the maximum power (Bharani and Jayasankar, 2015). In an automatic milk pipe line washing and milk preservative system fuzzy logic system can also be used (Yasmeen and Khan, 2013).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Based Energy Harvesting With the Movement of Plants Branches and Leaves

Ali¹,

Ashraf²,

Tayyaba³

et al. 2016

PAKJAS

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Energy crisis is increasing day by day around the globe due to overconsumption, overpopulation, wastage of energy, poor infrastructure, and delay in commissioning of power plants, unexplored renewable energy resources and techniques. There is an urgent need to find out the new ways of energy production and utilization techniques. Here, authors present the new technique of energy harvesting with the movement of plant's branches and leaves. Simulation and design algorithm of energy harvesting system has been presented. Electricity can be produced due to air pressure on piezo nanogenerator that can be installed on branches and leaves of plants. The effect of variation in inputs on the range of outputs has been studied. Fuzzy Logic tool in MATLAB and Mamdani's model have been used for the simulation and calculation. Air velocity and angle have been considered as the two inputs and power has been considered as output of this energy harvesting system. Three membership functions for the inputs and output parameters have been allotted. The energy harvesting system works according to the specific defined rules. The variations of the inputs and the outputs have been displayed on the surface viewer graphs. The results obtained by fuzzy based simulation and Mamdani's model based calculations have only 0.27% error. This shows that the proposed energy harvesting system will perform well.

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Yaw Control of Wind Turbine Using Fuzzy Logic Controller

Cited by 8 publications

References 7 publications

Development of Active Wind Vane for Low-Power Wind Turbines

Development of Active Wind Vane for Low-Power Wind Turbines

Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control

Fuzzy Logic Based Energy Harvesting With the Movement of Plants Branches and Leaves

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