Analysis of wind turbine dataset and machine learning based forecasting in SCADA-system

Singh, Upma; Rizwan, M.

doi:10.1007/s12652-022-03878-x

Cited by 22 publications

(2 citation statements)

References 34 publications

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“…Static errors need to be eliminated by kinematic calibration which can be transformed into a multi-objective optimization problem. Multi-objective optimization methods are widely used in transportation [10], power systems [31,35], geophysics [38] and performance prediction [2,20] domains. It is necessary to reduce the function dimension [19,27,30] when solve multi-objective optimization problems of complex systems.…”

Section: Static Errors Self-calibrationmentioning

confidence: 99%

Calibration of Static Errors and Compensation of Dynamic Errors for Cable-driven Parallel 3D Printer

Qian,

Jiang,

Qian

et al. 2024

J Intell Robot Syst

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As rigid robots suffer from the higher inertia of their rigid links, cable-driven parallel robots (CDPRs) are more suitable for large-scale three-dimensional (3D) printing tasks due to their outstanding reconfigurability, high load-to-weight ratio, and extensive workspace. In this paper, a parallel 3D printing robot is proposed, comprising three pairs of driving cables to control the platform motion and three pairs of redundant cables to adjust the cable tension. To improve the motion accuracy of the moving platform, the static kinematic error model is established, and the error sensitivity coefficient is determined to reduce the dimensionality of the optimization function. Subsequently, the self-calibration positions are determined based on the maximum cable length error in the reachable workspace. A self-calibration method is proposed based on the genetic algorithm to solve the kinematic parameter deviations. Additionally, the dynamic errors are effectively reduced by compensating for the elastic deformation errors of the cable lengths. Furthermore, an experimental prototype is developed. The results of dynamic error compensation after the self-calibration indicate a 67.4% reduction in terms of the maximum error along the Z-axis direction. Finally, the developed prototype and proposed calibration and compensation methods are validated through the printing experiment.

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Section: Static Errors Self-calibrationmentioning

confidence: 99%

Calibration of Static Errors and Compensation of Dynamic Errors for Cable-driven Parallel 3D Printer

Qian,

Jiang,

Qian

et al. 2024

J Intell Robot Syst

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“…Among these techniques, methods utilizing modal parameters such as frequencies [6], mode shapes [7], and modal curvatures [8] have been widely applied in civil engineering, aviation, and other domains. In the realm of computational techniques, various optimization algorithms [9][10][11][12][13][14][15], signal analysis [16][17][18][19], and data processing methods [20,21] are employed. The field of structural damage identification also encompasses the integration of multiple methods and technologies.…”

Section: Introductionmentioning

confidence: 99%

An improved convolutional neural network approach for damage localization in composite materials based on modal analysis

Guo,

Cao,

Gao

et al. 2023

Meas. Sci. Technol.

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Damage detection of composite materials using modal parameters has limitations in terms of sensitivity to small or localized damage and limited accuracy in damage localization. To address this issue, an enhanced channel attention residual network (ECARNet) damage detection model for composite laminates is proposed. First, finite element analysis is used to obtain training samples, which are processed as two-dimensional data to take full advantage of the convolutional neural network. Then, the residual module uses a multilayer perceptron instead of the traditional convolutional layers to learn the correlation between channels to enhance the generalization ability of the model, and uses the tanh activation function to retain negative information. Finally, a channel focus mechanism is introduced to enable the network to learn key features adaptively. Experimental results on two datasets with different levels of damage demonstrate the superior detection performance of ECARNet, achieving average detection accuracies of 98.13% and 97.94% respectively. A comparison with other methods verifies the effectiveness and reliability of the proposed approach. Furthermore, the effectiveness of the new method is validated on real-world test data.

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A Survey on Machine Learning Applications in Renewable Energies Forecasting

Mohabbati

2024

Power Systems

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Analysis of wind turbine dataset and machine learning based forecasting in SCADA-system

Cited by 22 publications

References 34 publications

Calibration of Static Errors and Compensation of Dynamic Errors for Cable-driven Parallel 3D Printer

Calibration of Static Errors and Compensation of Dynamic Errors for Cable-driven Parallel 3D Printer

An improved convolutional neural network approach for damage localization in composite materials based on modal analysis

A Survey on Machine Learning Applications in Renewable Energies Forecasting

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