Application of Neural Network Fitting for Pitch Angle Control of Small Wind Turbines

Salem, Mohamed E. M.; El-Batsh, Hesham M.; El-Betar, Ahmed; Attia, Ali M.A.

doi:10.1016/j.ifacol.2021.10.350

Cited by 9 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Salem et al [6] research focused on how to provide a good pitch angle controlling action for wind turbines in order to achieve the desired speed curve. It has been concluded the neural net (NN) fitting provided an accordant pitch angle control exertion, which made the response of the wind turbine follow the desired speed and gave an unavoidable change between pitch angle and speed.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Performance enhancement of small-scale wind turbine based on artificial neural network

Ibrahim,

Saleh,

Hamoody

2023

IJPEDS

View full text Add to dashboard Cite

<p><span lang="EN-US">Small-scale wind turbine is typically designed to resisted extreme wind; this work aims to adjust their pitch angle based on simulations that use standardization codes for wind turbines. Proportional integral derivative (PID) and artificial neural network (ANN) controllers are used to control the speed of wind turbines. The ideal action for controlling the blade pitch angle can be attained by providing the controller with speed information ahead of time, allowing the controller to provide the best action for blade pitch angle control. The results of this work represent the relationship between the turbine speed with respect to time at different pitch angle. It has been concluded that the ANN controller produced the best time response as compared with the PID controller.</span></p>

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…Due to the over speeding of the turbine rotor, small wind turbines cannot withstand strong winds velocity. The tiny WT system becomes more difficult and costs more as a result of the addition of pitch control [6]. Figure 1 shows depicts the wind turbines (WT's) construction.…”

Section: Introductionmentioning

confidence: 99%

Performance enhancement of small-scale wind turbine based on artificial neural network

Ibrahim,

Saleh,

Hamoody

2023

IJPEDS

View full text Add to dashboard Cite

show abstract

“…These parameters are counted as maximum power point tracking (MPPT), [ 185 ] speed/torque control, [ 186 ] power electronic converter control, [ 180,187 ] and pitch angle control. [ 188 ] The main way to enhance the efficiency of the wind plant is to apply an MPPT algorithm to the measured and collected wind data. In order to obtain the optimum power from wind energy, many complicated algorithms are used in the literature.…”

Section: Requirements and Technical Challengesmentioning

confidence: 99%

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

et al. 2022

View full text Add to dashboard Cite

Wind energy generation is a fast developing efficient renewable source widely used for modern power generation. [1] Countries and energy companies have increased their interest in electricity generation from wind energy in the last decades, thanks to its natural, cost-free, and environmentally friendly features. [2] Wind power plants can generate electrical energy at any time of the day and are distributed energy sources for systems that need permanent energy. [3] Nonetheless, due to the disordered nature of the earth's atmosphere, there is a major uncertainty in the electrical power produced by the wind, which causes complexities in the utilization and management of wind energy sources. [4] The changes in weather conditions depending on time and location also create uncertainty in managing the supply-demand balance controlled by the grid operators. [5] In cases where the electrical energy produced by wind power plants is insufficient, auxiliary energy sources provide operational support to the electric grid. [6] Ensuring the supply-demand balance in the electricity grid is also an important condition for the electrical power quality and energy supply in the transmission system, and wind power plant operators care about the follow-up of wind power in terms of economy, operation, and planning. [7] For this, the presence of wind power forecasting (WPF) is an essential factor in the optimization of wind power plants. [8] For consumers to use electrical energy uninterruptedly, efficiently, and economically, wind power plant operators need to estimate the generated electrical energy smoothly. [9] The smooth estimation of electrical power generated by wind turbines increases the operational efficiency for system maintenance, planning, investment, and energy traders. [10,11] It is a precious procedure for electrical grids to provide a respectable commercial yield on the electrical energy market. [12] The capability of advanced WPF provides the contributions like balancing the power system and mitigation of penalties, scheduling the maintenance of wind power plants, investment regarding site location, and technoeconomic and socioeconomic benefits. [13,14] In this way, it is possible to reduce the energy cost and obtain high revenue in the energy market as a result of the contributions obtained for wind power generation systems. [15]

show abstract

“…Neural network simulation is a significant field of study and development that ranges from biological studies to artificial applications. Using experimental data, artificial neural networks are developed to answer application problems in modeling and control [33]. Artificial neural networks are mathematical equation-based models of brain components such as neurons and their connections, as well as network input, control parameters, and network dynamics [34].…”

Section: Introductionmentioning

confidence: 99%

Application of neural network fitting for modeling the pneumatic networks bending soft actuator behavior

Salem

Wang

2022

Eng. Res. Express

View full text Add to dashboard Cite

Soft actuators have recently gained a lot of interests as an emerging topic, although complete methodologies for modeling soft actuators are still missing. Identifying and forecasting the behaviour of soft actuators is difficult due to the nonlinear behaviour of the materials used, the complicated geometries they form, and the wide range of motions they produce. In this paper, we demonstrated how to use neural network technology to describe the motion and produced force that the pneumatic network bending soft actuator can create at various input pressures. To confirm the results, three separate neural network models for three different modeling modes were constructed and evaluated with different input data sets. First, the dimension model, which deals with changes in the form and geometry of the soft actuator and their influence on its response at various pressure inputs. Second, the free force model, which simulates the motion of a soft actuator in free space without any external disturbances. Finally, the blocked force model, which may simulate a real-world soft actuator that is subjected to an external force. The input data sets were created with ABAQUS/CAE software, which replicates the behavior of the soft actuator and uses this data to train the neural network models.

show abstract

Application of Neural Network Fitting for Pitch Angle Control of Small Wind Turbines

Cited by 9 publications

References 10 publications

Performance enhancement of small-scale wind turbine based on artificial neural network

Performance enhancement of small-scale wind turbine based on artificial neural network

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

Application of neural network fitting for modeling the pneumatic networks bending soft actuator behavior

Contact Info

Product

Resources

About