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

Zhang, Fan; Wen, Zhang; Liu, Deshun; Jiao, Jie; Wan, Hengzheng; Zeng, Bing

doi:10.3390/app10010410

Cited by 22 publications

(13 citation statements)

References 22 publications

(34 reference statements)

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“…Therefore, a coordinated monitoring system should record the temperature of all components and perform an integrated assessment. In addition, with WTs in different locations, the sensor data received via the SCADA system, the interpretation of the SCADA data and the trustworthy analysis of the alarms can be another problem in this domain [15].…”

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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“…However, many advanced fault detection approaches are difficult to be implemented in practice due to the need of additional equipment that may incur considerable costs (Sun et al, 2016). Therefore, fault detection based on data from supervisory control and data acquisition (SCADA) systems, which have been installed in most MW-scale wind turbines, has attracted extensive research attention (Gray and Watson, 2010;Feng et al, 2011;Yang et al, 2013;Schlechtingen et al, 2013;Schlechtingen and Santos, 2014;Leahy et al, 2016;Hu et al, 2016;Wang et al, 2017;Pandit and Infield, 2018;Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…As this relationship varies with turbine health conditions, the model coefficients can be used as indicators for wind turbine fault detection. Zhang et al (2020) established state model curves by the bin method with SCADA data for wind turbines. The health index based on the Euclidean distance between the baseline state model curve and the investigated state model curve are proposed for the wind turbine health monitoring.…”

Section: Introductionmentioning

confidence: 99%

SCADA-data-based wind turbine fault detection: A dynamic model sensor method

Zhang

Lang

2020

Control Engineering Practice

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Fault detection based on data from the supervisory control and data acquisition (SCADA) system, which has been installed in most MW-scale wind turbines, has brought significant benefits for wind farm operators. However, the changes in the features of hardware sensor measurements, which are used in current SCADA systems, often cannot provide reliable early alarms. In order to resolve this problem, in this paper, a novel dynamic model sensor method is proposed for the SCADA data based wind turbine fault detection. A dynamic model representing the relationship between the generator temperature, wind speed, and ambient temperature is derived following the first principles and used as the basic structure of the model sensor. When the model sensor is applied for fault detection, its parameters are updated regularly using the generator temperature, wind speed, and ambient temperature data from the SCADA system. Then, from the updated model, the fault sensitive features of wind turbine system are extracted via performing system frequency analysis and used for the turbine fault detection. This novel model sensor method is applied to the SCADA data of a wind farm of 3 wind turbines currently operating in Spain. The results show that the proposed method can not only detect the turbine generator fault but also reveal the trend of ageing with the wind turbine generator, demonstrating its capability of failure prognosis for wind turbine system and components.

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“…CM is a process of monitoring the operating parameters of the physical system, it attracts a lot of research in the industrial field. CM is applied to anomaly detection [4][5][6] and fault diagnosis [7,8] of wind turbines. In [4], an evaluation index of wind turbine generator operating health based on the relationships with SCADA data was presented.…”

Section: Introductionmentioning

confidence: 99%

“…CM is applied to anomaly detection [4][5][6] and fault diagnosis [7,8] of wind turbines. In [4], an evaluation index of wind turbine generator operating health based on the relationships with SCADA data was presented. In [5], a framework was developed to monitor the health of a wind turbine using an undercomplete autoencoder.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Anomaly Detection Based on SCADA Data Mining

Liu

Ren

et al. 2020

Electronics

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In this paper, a wind turbine anomaly detection method based on a generalized feature extraction is proposed. Firstly, wind turbine (WT) attributes collected from the Supervisory Control And Data Acquisition (SCADA) system are clustered with k-means, and the Silhouette Coefficient (SC) is adopted to judge the effectiveness of clustering. Correlation between attributes within a class becomes larger, correlation between classes becomes smaller by clustering. Then, dimensions of attributes within classes are reduced based on t-Distributed-Stochastic Neighbor Embedding (t-SNE) so that the low-dimensional attributes can be more full and more concise in reflecting the WT attributes. Finally, the detection model is trained and the normal or abnormal state is detected by the classification result 0 or 1 respectively. Experiments consists of three cases with SCADA data demonstrate the effectiveness of the proposed method.

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

Cited by 22 publications

References 22 publications

Optimal Temperature-Based Condition Monitoring System for Wind Turbines

Optimal Temperature-Based Condition Monitoring System for Wind Turbines

SCADA-data-based wind turbine fault detection: A dynamic model sensor method

Wind Turbine Anomaly Detection Based on SCADA Data Mining

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