An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach

Chen, Jianjun; Hu, Weihao; Cao, Dengqing; Zhang, Bin; Huang, Qi; Chen, Zhe; Blaabjerg, Frede

doi:10.3390/en12142764

Cited by 40 publications

(15 citation statements)

References 41 publications

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“…However, the reliability of the obtained results strongly depends on the choice of the type of neural network and its settings [15]. Deep neural networks are now becoming one of the most popular methods of machine learning [4,[16][17][18][19]. They show better results in comparison with alternative methods in recognition problems [20].…”

Section: Methodsmentioning

confidence: 99%

Assessment of the Condition of Pipelines Using Convolutional Neural Networks

et al. 2020

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Pipelines are structural elements of many systems. For example, they are used in water supply and heat supply systems, in chemical production facilities, aircraft manufacturing, and in the oil and gas industry. Accidents in piping systems result in significant economic damage. An important factor for ensuring the reliability of energy transportation systems is the assessment of real technical conditions of pipelines. Methods for assessing the state of pipeline systems by their vibro-acoustic parameters are widely used today. Traditionally, the Fourier transform is used to process vibration signals. However, as a rule, the oscillations of the pipe-liquid system are non-linear and non-stationary. This reduces the reliability of devices based on the implementation of classical methods of analysis. The authors used neural network methods for the analysis of vibro-signals, which made it possible to increase the reliability of diagnosing pipeline systems. The present work considers a method of neural network analysis of amplitude-frequency measurements in pipelines to identify the presence of a defect and further clarify its variety.

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Section: Methodsmentioning

confidence: 99%

Assessment of the Condition of Pipelines Using Convolutional Neural Networks

et al. 2020

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“…Two papers are found that address the blade faults of variable rotational speed wind turbines. First is by Chen et al [25] in which they used the deep learning approach by embedding attention mechanism into long-short-term memory neural network to identify the wind turbine blade imbalance fault due to the accumulation of ice on the blades. Second paper by Joshuva et al [26] presented a study investigating various wind turbine blade faults using first variational mode decomposition (VMD) for experimental data signal pre-processing and then multi-layer perceptron (MLP) for blade fault classification.…”

Section: Introductionmentioning

confidence: 99%

Rotating blade faults classification of a rotor-disk-blade system using artificial neural network

Mas’ud

Jamal

Adewusi

et al. 2021

IJPEDS

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In this paper, the artificial neural network (ANN) has been utilized for rotating machinery faults detection and classification. First, experiments were performed to measure the lateral vibration signals of laboratory test rigs for rotor-disk-blade when the blades are defective. A rotor-disk-blade system with 6 regular blades and 5 blades with various defects was constructed. Second, the ANN was applied to classify the different x- and y-axis lateral vibrations due to different blade faults. The results based on training and testing with different data samples of the fault types indicate that the ANN is robust and can effectively identify and distinguish different blade faults caused by lateral vibrations in a rotor. As compared to the literature, the present paper presents a novel work of identifying and classifying various rotating blade faults commonly encountered in rotating machines using ANN. Experimental data of lateral vibrations of the rotor-disk-blade system in both x- and y-directions are used for the training and testing of the network.

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“…The uncertainty of wind speed makes the output power of the wind power generation system fluctuate greatly [1][2][3]. Frequent switching control will result in a transient overload of the transmission chain and an overshoot of output power.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Model Switching Predictive Active Power Control Scheme for Wind Generator System

Ren

et al. 2020

Energies

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To deal with the randomness and uncertainty of the wind power generation process, this paper proposes the use of the clustering method to complement the multi-model predictive control algorithm for active power control. Firstly, the fuzzy clustering algorithm is adopted to classify actual measured data; then, the forgetting factor recursive least square method is used to establish the multi-model of the system as the prediction model. Secondly, the model predictive controller is designed to use the measured wind speed as disturbance, the pitch angle as the control variable, and the active power as the output. Finally, the parameters and measured data of wind generators in operation in Western China are adopted for simulation and verification. Compared to the single model prediction control method, the adaptive multi-model predictive control method can yield a much higher prediction accuracy, which can significantly eliminate the instability in the process of wind power generation.

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An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach

Cited by 40 publications

References 41 publications

Assessment of the Condition of Pipelines Using Convolutional Neural Networks

Assessment of the Condition of Pipelines Using Convolutional Neural Networks

Rotating blade faults classification of a rotor-disk-blade system using artificial neural network

Adaptive Multi-Model Switching Predictive Active Power Control Scheme for Wind Generator System

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