A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&amp;M for Wind Turbines

Zhang, Pinjia; Lu, Delong

doi:10.3390/en12142801

Cited by 47 publications

(23 citation statements)

References 77 publications

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“…An exceeded temperature over the safe band may address an anomaly in the corresponding component of the WT, e.g., rotor over-speed, aging, short circuits, and lubrication failure. Thus, the temperature monitoring method is an acceptable approach for health monitoring of the WTs and can be applied for the diagnosis stage of maintenance management system [10].…”

Section: Introductionmentioning

confidence: 99%

Optimal Temperature-Based Condition Monitoring System for Wind Turbines

et al. 2021

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With the increasing demand for the efficiency of wind energy projects due to challenging market conditions, the challenges related to maintenance planning are increasing. In this paper, a condition-based monitoring system for wind turbines (WTs) based on data-driven modeling is proposed. First, the normal condition of the WTs key components is estimated using a tailor-made artificial neural network. Then, the deviation of the real-time measurement data from the estimated values is calculated, indicating abnormal conditions. One of the main contributions of the paper is to propose an optimization problem for calculating the safe band, to maximize the accuracy of abnormal condition identification. During abnormal conditions or hazardous conditions of the WTs, an alarm is triggered and a proposed risk indicator is updated. The effectiveness of the model is demonstrated using real data from an offshore wind farm in Germany. By experimenting with the proposed model on the real-world data, it is shown that the proposed risk indicator is fully consistent with upcoming wind turbine failures.

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Section: Introductionmentioning

confidence: 99%

Optimal Temperature-Based Condition Monitoring System for Wind Turbines

et al. 2021

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“…The traditional wind power plant maintenance strategy relies heavily on regular maintenance and after-maintenance, and the deployment of spare parts has a long cycle, which leads to the high cost of failure maintenance and has a huge impact on the operation and maintenance economy of wind power plants [7]. Therefore, how to make early warning before the occurrence of wind turbine failure is of great significance to reduce the operation and maintenance cost of wind farms and the longterm development of wind energy industry [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Diagnosis of Wind Turbine Based on SCADA Data

Liu

Wang

2020

IEEE Access

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Effective early warning of wind turbine failures is of great significance to reduce the operation and maintenance costs of wind farms and improve power generation efficiency. At present, most wind farms are installed with supervisory control and data acquisition (SCADA) system, and SCADA data contains a lot of hidden information, which can be used for fault early warning. This paper uses the generator temperature and gearbox oil temperature in the SCADA data as the entry point for fault warning. Firstly, the eXtreme gradient boosting (XGBoost) algorithm is used to establish the normal temperature regression prediction model of wind turbine components. Then, the residual between the predicted value and the actual value is calculated, and the change trend of the residual is monitored by the principle of exponentially weighted moving-average (EWMA) control chart. Finally, by setting an appropriate threshold, the variation trend of the residual is judged to determine the occurrence and development of the fault. This paper uses two fault detection methods: fixed threshold and dynamic threshold based on adaptive algorithm, and compares the advantages and disadvantages of the two methods. Based on the SCADA data of a wind farm in Inner Mongolia (China), this paper designs the fault early warning test of the wind turbine generator and gearbox. The experimental results show that for the generator, the fixed fault threshold method can give the fault alarm 3 hours in advance, while the dynamic fault threshold determination method can give fault alarm 4.25 hours in advance. For gearbox, the fixed fault threshold method can give the fault alarm 2 hours in advance, while the dynamic threshold fault diagnosis method can send out the fault alarm 2.75 hours in advance. INDEX TERMS Wind turbine; Fault warning; eXtreme gradient boosting (XGBoost); exponentially weighted moving-average (EWMA); supervisory control and data acquisition (SCADA)

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“…To determine the real-time operation status of wind turbines and to implement reasonable maintenance of wind turbines, modern large wind turbines are equipped with SCADA (Supervisory Control and Data Acquisition) systems. The application of SCADA data for wind turbine state identification, fault diagnosis, and early warning is currently a hot research topic in academic circles [2][3][4] and focuses mainly on statistical methods or data mining methods, such as neural networks and support vector machines [5,6]. In terms of statistical methods, Kusiak and others identified faulty wind turbines through comparative analysis of SCADA data of more than three wind turbines [7,8].…”

Section: Introductionmentioning

confidence: 99%

Calculation and Analysis of Wind Turbine Health Monitoring Indicators Based on the Relationships with SCADA Data

et al. 2020

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This paper proposes an evaluation index of wind turbine generator operating health based on the relationships with SCADA (Supervisory Control and Data Acquisition) data. First, the relationship among the data from a wind turbine SCADA system is thoroughly analyzed. Then, a time based sliding window model is used to process the SCADA data by the bin method, and a running state model of the wind turbine is established by data fitting. Taking the normal operation state model of the wind turbine as the standard reference and based on the Euclidean distance between the state model curve and the standard model curve, the health index of the wind turbine operation state is proposed. Finally, using SCADA data from two 2 MW direct-drive wind turbines as examples for analysis and discussion, the results show that: (1) health indicators have good stability and sensitivity to wind turbine operating conditions; (2) the width of the data window in the sliding window model must cover all operating conditions of the wind turbine to ensure that the health index depicts the operating state of the wind turbine; (3) the data window width, window increment, and data fitting modeling all affect the health indicators, and thus, the selection of the sliding window model parameters and the data relationship modeling methods should consider the accuracy and real-time performance of the health indicators; and (4) the data acquisition cycle does not affect the health indicators. Once the basic characteristics of the data relations are known, direct data fitting modeling is more efficient than bin preprocessing modeling.

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A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines

Cited by 47 publications

References 77 publications

Optimal Temperature-Based Condition Monitoring System for Wind Turbines

Optimal Temperature-Based Condition Monitoring System for Wind Turbines

Research on Fault Diagnosis of Wind Turbine Based on SCADA Data

Calculation and Analysis of Wind Turbine Health Monitoring Indicators Based on the Relationships with SCADA Data

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