A Review of Fault Diagnosis, Status Prediction, and Evaluation Technology for Wind Turbines

Zhang, Fanghong; Chen, Mingsong; Zhu, Yuze; Zhang, Kai; Li, Qingan

doi:10.3390/en16031125

Cited by 12 publications

(5 citation statements)

References 38 publications

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“…mat format. Click on the converted file in the workspace to obtain the corresponding data [6][7][8]. Be sure to save the converted file to the correct folder.…”

Section: Fault Diagnosis Steps Based On Som Neural Networkmentioning

confidence: 99%

Fault Diagnosis of Hydraulic System based on SOM Neural Network

Chen¹,

Xiao²,

Li³

et al. 2023

FCIS

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The working principle of hydraulic system is to use the flow and pressure of liquid in the system for energy transfer and conversion. Hydraulic system realizes various work tasks such as pushing, grasping, lifting, rotating, etc. by controlling the action of hydraulic actuators. Its advantages are high power density, good stability, fast response time, smooth power output, etc. The disadvantage is that the hydraulic system is easy to lose control of more points. In this paper, taking the horizontal outrigger hydraulic circuit as an example, the hydraulic pump leakage, etc. is used as a fault sample, and the SOM neural network is used for fault diagnosis to make timely and accurate diagnosis of the abnormal or fault state of the hydraulic system, give guidance on the operation of the hydraulic system, improve the reliability and safety of operation, and reduce the fault loss to a minimum.

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“…mat format. Click on the converted file in the workspace to obtain the corresponding data [6][7][8]. Be sure to save the converted file to the correct folder.…”

Section: Fault Diagnosis Steps Based On Som Neural Networkmentioning

confidence: 99%

Fault Diagnosis of Hydraulic System based on SOM Neural Network

Chen¹,

Xiao²,

Li³

et al. 2023

FCIS

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“…As a clean, reliable, and efficient form of energy, nuclear energy has unique advantages. In some developed countries, nuclear energy has become an important part of their power systems, and it has also driven the development of related fields [3].…”

Section: Introductionmentioning

confidence: 99%

PT-Informer: A Deep Learning Framework for Nuclear Steam Turbine Fault Diagnosis and Prediction

Zhou,

An,

Yang

et al. 2023

Machines

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The health status of equipment is of paramount importance during the operation of nuclear power plants. The occurrence of faults not only leads to significant economic losses but also poses risks of casualties and even major accidents, with unimaginable consequences. This paper proposed a deep learning framework called PT-Informer for fault prediction, detection, and localization in order to address the challenges of online monitoring of the operating health of nuclear steam turbines. Unlike traditional approaches that involve separate design and execution of feature extraction for fault diagnosis, classification, and prediction, PT-Informer aims to extract fault features from the raw vibration signal and perform ultra-real-time fault prediction prior to their occurrence. Specifically, the encoding and decoding structure in PT-Informer ensures the capture of temporal dependencies between input features, enabling accurate time series prediction. Subsequently, the predicted data are utilized for fault detection using PCA in the PT-Informer framework, aiming to assess the likelihood of equipment failure in the near future. In the event of potential future failures, t-SNE is utilized to project high-dimensional data into a lower-dimensional space, facilitating the identification of clusters or groups associated with different fault types or operational conditions, thereby achieving precise fault localization. Experimental results on a nuclear steam turbine rotor demonstrate that PT-Informer outperformed the traditional GRU with a 4.94% improvement in R2 performance for prediction. Furthermore, compared to the conventional model, the proposed PT-Informer enhanced the fault classification accuracy of the nuclear steam turbine rotor from 97.4% to 99.6%. Various comparative experiments provide strong evidence for the effectiveness of PT-Informer framework in the diagnosis and prediction of nuclear steam turbine.

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“…As temperatures rise, the turbine performance slightly deviates from the manufacturer's guaranteed curve [11]. The higher winding temperatures can decrease generator efficiency, and control inverter life may be halved (normal operating range about −40 • C to +55 • C) [12]. There are various reasons for an increase in temperatures in wind turbines.…”

Section: Introductionmentioning

confidence: 99%

Impact of the Air Filtration in the Nacelle on the Wind Turbine Performance

et al. 2023

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Wind turbine farms require efficient operation and maintenance strategies to ensure long-term profitability and cost-effectiveness. However, temperatures and dust accumulation can significantly affect turbine performance and increase maintenance costs. This study investigates the impact of air filter clogging on wind turbine nacelle temperature and performance by collecting data from the Supervisory Control and Data Acquisition (SCADA) system of wind farms in southern Jordan, including temperature, wind speed, and power generation. The findings demonstrate that uncleaned air filters can lead to inefficient heat dissipation, higher nacelle temperatures, and reduced power production. Turbines with uncleaned filters experienced an average nacelle temperature increase of 15.44 °C compared to 13.30 °C for turbines with clean filters, resulting in a difference in the power production of 66.54 kW.

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A Review of Fault Diagnosis, Status Prediction, and Evaluation Technology for Wind Turbines

Cited by 12 publications

References 38 publications

Fault Diagnosis of Hydraulic System based on SOM Neural Network

Fault Diagnosis of Hydraulic System based on SOM Neural Network

PT-Informer: A Deep Learning Framework for Nuclear Steam Turbine Fault Diagnosis and Prediction

Impact of the Air Filtration in the Nacelle on the Wind Turbine Performance

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