Analysis of a State Degradation Model and Preventive Maintenance Strategies for Wind Turbine Generators Based on Stochastic Differential Equations

Su, Hongsheng; Zhao, Yifan; Wang, Xueqian

doi:10.3390/math11122608

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…Reference [15] proposes a combined approach for wind turbine generator maintenance by integrating time-based maintenance (TBM) and condition-based maintenance (CBM) strategies. A stochastic state model based on stochastic differential equations (SDE) accurately represents generator degradation.…”

Section: Maintenance Scheduling a Reliability Centered Maintenancementioning

confidence: 99%

A survey on data acquisition methods in conditional monitoring of wind turbines

Heibati,

Alipour-Sarabi,

Bathaee

2023

Scientia Iranica

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The generation of electrical power through wind turbines has significantly increased nowadays. However, these systems are prone to faults that can disrupt the network and incur substantial costs for the generation units. Therefore, effective maintenance scheduling becomes crucial. A major challenge faced by wind turbines is their maintenance requirements, as any interruption in their operation and power generation can result in significant economic losses. Consequently, meticulous planning is indispensable to minimize such consequences. This paper that is the first part of the study conducts a survey of data acquisition methods in condition monitoring of wind turbines. In the second part, signal processing techniques for condition monitoring of wind turbines are presented. Furthermore, the paper examines a range of studies that have implemented practical condition monitoring methods in wind turbines, delving into the associated challenges and proposing potential solutions. Various methods such as vibration analysis, acoustic analysis, electrical parameter analysis, AI-based techniques, and fault-tolerant control have been employed for wind turbine maintenance. However, limitations exist in terms of data availability and computational burden. Future challenges include developing algorithms that require less data, reducing computational requirements, updating models with new conditions, enabling early detection and proactive maintenance, and reducing maintenance costs.

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Section: Maintenance Scheduling a Reliability Centered Maintenancementioning

confidence: 99%

A survey on data acquisition methods in conditional monitoring of wind turbines

Heibati,

Alipour-Sarabi,

Bathaee

2023

Scientia Iranica

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“…Condition monitoring methods for wind turbines can be broadly categorized into three groups: empirical knowledge-based methods [4,5], physical model-based methods [6][7][8], and data driven-based methods [9][10][11]. Empirical knowledge-based methods have disadvantages of excessive dependence on the knowledge and experience of domain experts, making it difficult to mine complex and highly reliable equipment failure mechanism features, and have significant limitations with the application in the WTCM field.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Gearbox Condition Monitoring Using Hybrid Attentions and Spatio-Temporal BiConvLSTM Network

Yan,

Liu,

Ren

et al. 2023

Energies

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Gearbox fault deterioration can significantly impact the safety, reliability, and efficiency of wind turbines, resulting in substantial economic losses for wind farms. However, current condition monitoring methods face challenges in effectively mining the hidden spatio-temporal features within SCADA data and establishing reasonable weight allocations for model input variables. To tackle these issues, we proposed a novel condition monitoring method for wind turbine gearboxes called HBCE, which integrated a feature-time hybrid attention mechanism (HA), the bidirectional convolutional long short-term memory networks (BiConvLSTM), and an improved exponentially weighted moving-average (iEWMA). Specifically, utilizing historical health SCADA data acquired through the modified Thompson tau data-cleaning algorithm, a normal behavior model (HA-BiConvLSTM) of gearbox was constructed to effectively extract the spatio-temporal features and learn normal behavior patterns. An iEWMA-based outlier detection approach was employed to set dynamic adaptive thresholds, and real-time monitor the prediction residuals of HA-BiConvLSTM to identify the early faults of gearbox. The proposed HBCE method was validated through actual gearbox faults and compared with conventional spatio-temporal models (i.e., CNN-LSTM and CNN&LSTM). The results illustrated that the constructed HA-BiConvLSTM model achieved superior prediction precision in terms of RMSE, MAE, MAPE, and R2, and the proposed method HBCE can effectively and reliably identify early anomalies of a wind turbine gearbox in advance.

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“…Dui et al [4] proposed a resilience optimization model of a multi-component system to determine the degree of influence of components on the reliability of the wind turbine system, thus improving the reliability of the wind turbine system. Su et al [5] established a stochastic differential equation state model to describe the degradation of wind turbines, preventing wind turbine failure. Research has shown that focusing on the faults and reliability of wind turbine components is necessary to increase the reliability of wind turbines.…”

Section: Introductionmentioning

confidence: 99%

Residual Strength Modeling and Reliability Analysis of Wind Turbine Gear under Different Random Loadings

Gao,

Liu,

Yuan

et al. 2023

Mathematics

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A novel method is proposed to investigate the pattern of variation in the residual strength and reliability of wind turbine gear. First, the interaction between loads and the effect of the loading sequence is considered based on the fatigue damage accumulation theory, and a residual strength degradation model with few parameters is established. Experimental data from two materials are used to verify the predictive performance of the proposed model. Secondly, the modeling and simulation of the wind turbine gear is conducted to analyze the types of fatigue failures and obtain their fatigue life curves. Due to the randomness of the load on the gear, the rain flow counting method and the Goodman method are employed. Thirdly, considering the seasonal variation of load, the decreasing trend of gear fatigue strength under multistage random load is calculated. Finally, the dynamic failure rate and reliability of gear fatigue failure under multistage random loads are analyzed. The results demonstrate that the randomness of residual strength increases with increasing service time. The seasonality of load causes fluctuations in the reliability of gear, providing a new idea for evaluating the reliability of the wind turbine gear.

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Analysis of a State Degradation Model and Preventive Maintenance Strategies for Wind Turbine Generators Based on Stochastic Differential Equations

Cited by 6 publications

References 24 publications

A survey on data acquisition methods in conditional monitoring of wind turbines

A survey on data acquisition methods in conditional monitoring of wind turbines

Wind Turbine Gearbox Condition Monitoring Using Hybrid Attentions and Spatio-Temporal BiConvLSTM Network

Residual Strength Modeling and Reliability Analysis of Wind Turbine Gear under Different Random Loadings

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