Performance improvement for large floating wind turbine by using a non‐linear pitch system based on neuro‐adaptive fault‐tolerant control

Wang, Lei; Jin, Fangjun; Chen, Jiawei; Gao, Yang; Du, Xin; Zhang, Xu; Yang, Jiongming

doi:10.1049/rpg2.12469

Cited by 7 publications

(3 citation statements)

References 38 publications

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“…CNN is a kind of deep learning model, which is widely used in image processing, computer vision and pattern recognition. Its design is inspired by the working principle of human visual system, and it has excellent feature extraction and pattern recognition capabilities [5][6] . The basic structure of CNN includes convolution layer, pooling layer and fully connected layer (Figure 1).…”

Section: Cnn Overviewmentioning

confidence: 99%

Real-time monitoring and fault diagnosis of new energy power generation system based on deep convolution neural network

Luo

2024

Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023)

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With the increasing demand for clean energy in society, the stable operation and efficient management of new energy power generation system(NEPGS) has become a key issue. This research focuses on the real-time monitoring and fault diagnosis o fNEPGS based on deep convolutional neural network (DCNN), aiming at improving the reliability of the system, reducing downtime and improving maintenance efficiency. First of all, by constructing DCNN model, the realtime monitoring of NEPGS is realized in this study. The model can extract key features and dynamically adapt to the changes of system operation state by learning large-scale monitoring data. The experimental results show that DCNN performs well under normal working conditions and successfully captures the characteristic patterns under different working conditions. Secondly, this study applies DCNN to fault diagnosis, covering a variety of potential fault modes, such as broken teeth, tooth surface wear and so on. By introducing various fault samples into the training set, DCNN can identify and classify different fault types with high accuracy. This provides feasibility for providing real-time and accurate diagnosis when faults occur. The experimental results show that the system in this study has achieved ideal performance on the verification set, all samples have been correctly classified, and the diagnostic accuracy rate has reached 100%. This provides an advanced and feasible solution for health management and real-time monitoring of NEPGS.

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Section: Cnn Overviewmentioning

confidence: 99%

Real-time monitoring and fault diagnosis of new energy power generation system based on deep convolution neural network

Luo

2024

Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023)

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“…Current research on fault-tolerant control of pitch systems has focused on wind power generation systems. In [33,34], a disturbance observer is used for fault diagnosis and combined adaptive neural networks with sliding mode control to achieve fault-tolerant control. In [35,36], a fault detection and isolation scheme based on a sliding mode observer is proposed for the case where actuator faults exist in both the pitch and drive train systems of a wind turbine.…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Control of Tidal Stream Turbines: Non-Singular Fast Terminal Sliding Mode and Adaptive Robust Method

Wang,

Wang

2024

JMSE

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This paper addresses the issues of maximum power point tracking (MPPT) and fault-tolerant control in tidal steam turbines under complex marine environments. In order to solve the conflicting problems in the existing sliding mode control between dynamic performance and chatter reduction as well as the use of fault estimation link in the fault-tolerant control, which increases the system complexity, an adaptive non-singular fast terminal sliding mode and adaptive robust fault tolerance method (ANFTSMC-ARC) is proposed. First, a speed controller equipped with adaptive non-singular fast terminal sliding mode control (ANFTSMC) is designed to improve the power capture efficiency under swell disturbances. This design achieves fast convergence and circumvents the singularity problem. Then, a new reach law is proposed based on the adaptive hybrid exponential reaching law (AHERL), which ensures high tracking performance while reducing chattering. In addition, considering that the hydraulic pitch system is prone to failure, a fault-tolerant controller with automatically adjustable gain is designed under the adaptive robust scheme. With the help of Lyapunov theory, the closed-loop system is proved to be uniform and ultimately bounded. Finally, comparative simulation results verify the efficiency of the proposed control strategy.

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“…To address fault location, a wavelet neural network is constructed and applied. It is worth noting that the fusion of wavelet analysis and neural networks can take various forms, and wavelet mother waves can be categorized into multiple types (Wang, L, et al, 2022). We can try to improve the traditional neural network with other mother waves in order to achieve better results (Yang, X, et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Power System Fault Diagnosis and Prediction System Based on Graph Neural Network

Hao,

Zhang,

Ping

2024

International Journal of Information Technologies and Systems Approach

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The stability and reliability of the power system are of utmost significance in upholding the smooth functioning of modern society. Fault diagnosis and prediction represent pivotal factors in the operation and maintenance of the power system. This article presents an approach employing graph neural network (GNN) to enhance the precision and efficiency of power system fault diagnosis and prediction. The system's efficacy lies in its ability to capture the intricate interconnections and dynamic variations within the power system by conceptualizing it as a graph structure and harnessing the capabilities of GNN. In this study, the authors introduce a substitution for the pooling layer with a convolution operation. A central role is played by the global average pooling layer, connecting the convolution layer and the fully connected layer. The fully connected layer carries out nonlinear computations, ultimately providing the classification at the top-level output layer. In experiments and tests, we verified the performance of the system.

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Performance improvement for large floating wind turbine by using a non‐linear pitch system based on neuro‐adaptive fault‐tolerant control

Cited by 7 publications

References 38 publications

Real-time monitoring and fault diagnosis of new energy power generation system based on deep convolution neural network

Real-time monitoring and fault diagnosis of new energy power generation system based on deep convolution neural network

Fault-Tolerant Control of Tidal Stream Turbines: Non-Singular Fast Terminal Sliding Mode and Adaptive Robust Method

Power System Fault Diagnosis and Prediction System Based on Graph Neural Network

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