Sensorless Estimation of Wind Speed by Soft Computing Methodologies: A Comparative Study

Petković, Dalibor; Arif, Muhammad; Shamshirband, Shahaboddin; Bani-Hani, Ehab Hussein; Kiakojoori, Davood

doi:10.15388/informatica.2015.60

Cited by 13 publications

(5 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…53,54 RBF kernel does not need complicated quadratic programming problem but required only several linear equations for a solution. 55 Hence, RBF is adopted in this study, with the parameter. Equation (16) shows the nonlinear radial basis kernel function:…”

Section: Svmmentioning

confidence: 99%

Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine

Band

Ghazvinei

Yusof

et al. 2020

Energy Science & Engineering

View full text Add to dashboard Cite

Theoretically, the ocean has the potential to generate 20,000 TWh to 92 000 TWh of electricity. While as of 2012, our planet only requires 16 000 TWh of electricity. 1,2 A large portion of the marine energy continues to be unexploited because of the high cost involved in generating electricity. 3,4 Tidal current or tidal stream technologies had great strides improvement on the way to commercialization in the last decade. Tidal range technologies utilize a barrage to produce power from the elevation variance among low and high tide. The innovations made for tidal range power generation are tidal "reefs," "lagoons," and "fences," as well as low head tidal barrages. 5 There are three groups of tidal energy technologies:

show abstract

Section: Svmmentioning

confidence: 99%

Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine

Band

Ghazvinei

Yusof

et al. 2020

Energy Science & Engineering

View full text Add to dashboard Cite

show abstract

“…Also, it should be noted that as the wind speed is not accurately measurable, so, ) in (23) is an unknown variable. Substituting (23) into (21) leads to,…”

Section: Desired Operational Mode and Control Objectivesmentioning

confidence: 99%

“…In [22] a nonstandard extended Kalman filter is developed to estimate the wind speed for maximum power extraction of variable speed wind turbines. In [23] a comparative study of using soft computing methodologies for estimation of wind speed was presented. A review of the effective wind speed estimation-based control of wind turbines can be found in [24].…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation

Habibi¹,

Rahimi²,

Howard³

et al. 2019

Preprint

View full text Add to dashboard Cite

This paper presents a novel adaptive fault-tolerant neural-based control design for wind turbines with unknown dynamic and unknown wind speed. By utilizing the barrier Lyapunov function in the analysis of the Lyapunov direct method, the constrained behavior of the system is provided in which the rotor speed, its variation and generated power remain in the desired bounds. In addition, input saturation is also considered in terms of smooth pitch actuator bounding. Furthermore, by utilizing a Nussbaum-type function in designing the control algorithm, the unpredictable wind speed variation is captured without requiring accurate wind speed measurement, observation or estimation. Moreover, with the proposed adaptive analytic algorithms, together with the use of radial basis function neural networks, a robust adaptive and fault-tolerant control scheme is developed without the need for precise information about the wind turbine model nor the pitch actuator faults. Additionally, the computational cost of the resultant control law is reduced by utilizing a dynamic surface control technique. The effectiveness of the developed design is verified using theoretical analysis tools and illustrated by numerical simulations on a high-fidelity wind turbine benchmark model with different fault scenarios. Comparison of the achieved results to the ones that can be obtained via an available industrial controller shows the advantages of the proposed scheme.

show abstract

“…ANN has been applied to model renewable energy systems, economics, psychology, subsurface two-phase flow and many more (Abidoye and Bello, 2017; Abidoye and Das, 2015; Kalogirou, 1999). Authors like Petković et al (2015) have suggested soft computing for wind farm analysis. Soft and simplified computational techniques will overcome the cost and complexity associated with flow-physics-based computational fluid dynamic (CFD) simulators that are employed in works like that of Das et al (2014) and Abidoye and Wairagu (2013).…”

Section: Introductionmentioning

confidence: 99%

Effects of environmental and turbine parameters on energy gains from wind farm system: Artificial neural network simulations

et al. 2019

View full text Add to dashboard Cite

Artificial neural network modelling has been employed to investigate the effects of various environmental and machine factors on the energy gain from wind farm systems. Numerical comparison of artificial neural network and nonlinear regression from XLSTAT showed that ANN possessed better numerical accuracy in predicting multivariate data. Several artificial neural network models are developed and tested with several structures to obtain the best prediction performance in energy gain from different wind farms in Jordan. The best performing artificial neural network model was used to predict the energy gain from wind farm based on changes in annual wind speed, turbine rotor diameter and turbine power. As a result of 20% increase in turbine power, 14.4%–31% energy gains were recorded across different wind farms. The proposed artificial neural network model was also a good predictor for energy cost resulting from specific wind farm design.

show abstract

Sensorless Estimation of Wind Speed by Soft Computing Methodologies: A Comparative Study

Cited by 13 publications

References 69 publications

Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine

Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine

Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation

Effects of environmental and turbine parameters on energy gains from wind farm system: Artificial neural network simulations

Contact Info

Product

Resources

About