Power Curve-Based Fault Detection Method for Wind Turbines

Bilendo, Francisco; Badihi, Hamed; Lu, Ningyun; Cambron, Philippe; Jiang, Bin

doi:10.1016/j.ifacol.2022.07.163

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…where n denotes the size of the moving window, x i represents the ith value at time i, whereas l designates the length of the considered signal. This approach (moving mean) has been used in [70,71] to reduce the stochastic effects in SCADA data.…”

Section: Filtering Approachmentioning

confidence: 99%

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Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review

et al. 2022

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In the wind energy industry, the power curve represents the relationship between the “wind speed” at the hub height and the corresponding “active power” to be generated. It is the most versatile condition indicator and of vital importance in several key applications, such as wind turbine selection, capacity factor estimation, wind energy assessment and forecasting, and condition monitoring, among others. Ensuring an effective implementation of the aforementioned applications mostly requires a modeling technique that best approximates the normal properties of an optimal wind turbines operation in a particular wind farm. This challenge has drawn the attention of wind farm operators and researchers towards the “state of the art” in wind energy technology. This paper provides an exhaustive and updated review on power curve based applications, the most common anomaly and fault types including their root-causes, along with data preprocessing and correction schemes (i.e., filtering, clustering, isolation, and others), and modeling techniques (i.e., parametric and non-parametric) which cover a wide range of algorithms. More than 100 references, for the most part selected from recently published journal articles, were carefully compiled to properly assess the past, present, and future research directions in this active domain.

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Section: Filtering Approachmentioning

confidence: 99%

“…LOESS employs "locally quadratic regression" as a "weighted linear least-squares regression", determining the smoothened sample through "weighted regression" at the predictor sample. This approach (LOESS) was used in [70] to attain a robust power curve reference and in [72] for smoothing purpose.…”

Section: Filtering Approachmentioning

confidence: 99%

Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review

et al. 2022

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Enhanced anomaly detection and normal behaviour power curve modelling in wind farm SCADA data: A hybrid approach

Mehmood,

Wang

2024

Preprint

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To achieve optimal performance and reduce the maintenance cost of wind turbines, anomaly detection and power curve modelling are crucial. The supervisory control and data acquisition (SCADA) system provides continuous and real-time data insights by collecting different wind-turbine operational parameters. This study introduces a novel strategy combining the strengths of Isolation Forest (iForest) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to identify and isolate anomalous data. The hybrid iForest-DBSCAN model processes enormous amounts of SCADA data to detect outliers and anomalies of wind turbines under different operating conditions. By utilizing normal data with minimum anomalies, normal behavious power curves (NBPC) were modelled using a robust Locally Estimated Scattered Smoothing (LOESS) technique. Robust power curves allow us to compare the performances of wind turbines and ensure an optimized function with minimum maintenance. Different datasets validated the proposed method with higher accuracy and fewer computational resource requirements than traditional methods. From the two wind farms, the iForest-DBSCAN model identified the datasets effectively while successfully generating NBPC with a 95% confidence interval. This study demonstrated the effectiveness of cutting-edge data-driven models and techniques for optimizing the efficiency and performance of wind farms.

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Power Curve-Based Fault Detection Method for Wind Turbines

Cited by 2 publications

References 8 publications

Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review

Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review

Enhanced anomaly detection and normal behaviour power curve modelling in wind farm SCADA data: A hybrid approach

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