A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements

Zou, M.; Djokić, Saša Ž.

doi:10.3390/en13164228

Cited by 18 publications

(10 citation statements)

References 80 publications

(451 reference statements)

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“…We had excluded the outliers from the presented results, enabling a clearer comparison of the temporal impact on reflectance changes in different bands. The outliers, denoting data points significantly distant from the overall distribution, were identified to ensure the robustness of the dataset (Zou and Djokic, 2020).…”

Section: Resultsmentioning

confidence: 99%

Data quality analysis after hyperspectral LiDAR sequentially mapping trees

Dong,

Lin

2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Light detection and ranging (LiDAR), as an innovative remote sensing tool, not only captures target reflectance but also provides its morphological parameters. Traditional single/multi-band LiDAR and multispectral LiDAR (MSL) are presently employed in applications such as 3D modeling and plant biochemical parameter inversion albeit with effectiveness limited. Moreover, hyperspectral LiDAR (HSL) distinguished by its expanded array of spectral detection channels and enhanced spectral resolution, has proven more effective in meeting these requirements and also exhibits superior capabilities in both feature and land cover classification tasks. Nevertheless, point clouds acquired through HSL frequently exhibit quality deficiencies, including uneven density and excessive noise. Meanwhile, there exists a notable absence of technical specifications and operational standards governing the measurement protocols for HSL systems globally. To address this gap, this study constructed a systematic analysis framework of data quality in hyperspectral point clouds and endeavors to qualitatively analyse 30 tree point clouds continuously scanned with Finnish Geospatial Research Institute (FGI) 8-band hyperspectral laser scanner. Furthermore, this research validated the theoretical feasibility of employing the 8-band HSL system for inversion processes aimed at quantifying chlorophyll leaf content. Apart from detecting the time-varying patterns of reflectance within birch canopy point clouds, the results of this study also effectively pinpointed the band exhibiting heightened noise level of the HSL system, demonstrating the efficacy of our proposed quality analysis methodology. The endeavor presented in this study can serve as a cornerstone for advancing hyperspectral LiDAR across a diverse array of related remote sensing and earth observation applications.

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Section: Resultsmentioning

confidence: 99%

Data quality analysis after hyperspectral LiDAR sequentially mapping trees

Dong,

Lin

2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In the case of positive power values, the time samples linked to failures and maintenance actions are mostly distributed along the normal operating curve of the WT. It shows that not all of these events require the shutdown of the WT, allowing normal or restricted operation, 31 depending on the severity of the failure reported. Most failure records occur for negative power values (2.58% vs. 0.76%) when the wind speed has an appropriate value to generate electricity safely. However, the WT is inoperative and consumes energy. The instances of maintenance are approximately equally distributed for both positive and negative power values (8.86% vs. 9.61%).…”

Section: Resultsmentioning

confidence: 99%

“…• In the case of positive power values, the time samples linked to failures and maintenance actions are mostly distributed along the normal operating curve of the WT. It shows that not all of these events require the shutdown of the WT, allowing normal or restricted operation, 31 depending the severity of the failure reported.…”

Section: Distribution Of Labelled Time Samples On Each Wt Power Curvementioning

confidence: 99%

Reliability of onshore wind turbines based on linking power curves to failure and maintenance records: A case study in central Spain

et al. 2023

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Summary Wind turbine (WT) reliability has come to the forefront of research due to the rapid growth of wind energy in recent years. Reliability information can help understand failure causes and focus maintenance and prevention efforts on the most critical components, reducing costs and increasing profits. This paper offers new insights into WT reliability after analysing the data provided by the Supervisory Control And Data Acquisition (SCADA) system collected from seven onshore WTs located in central Spain from January 2014 to September 2021. To this end, we propose a method to link SCADA data to failure and maintenance records based on checking whether each 10‐min average time sample was collected when any failure or maintenance action had been reported. These records have been manually mapped to the WT taxonomy based on the standard Reference Designation System for Power Plants (RDS‐PP®) with minor changes. We present three different results: (i) The capacity factor and time‐based availability of each WT; (ii) the subsystem failure rate and downtime to identify the most critical ones; and (iii) each WT power curve with the 10‐min time samples labelled as healthy, under maintenance, or failure states, along with a ranking of the subsystems causing the most failures in each part of the power curves. It is the first time that time samples are linked to failure and maintenance records to visualise their distribution on the power curves. These results can help research point in the right direction to improve reliability and increase electricity production worldwide.

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“…Precisely, we discard outliers and impute missing values to improve data quality. Outliers in wind turbine measurements could be due to wide ranges of causes, such as malfunctioning measurement sensors [84]. Outliers are generally identified and eliminated to increase the considered model's forecasting accuracy [85].…”

Section: Wind Power Prediction Strategymentioning

confidence: 99%

Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study

2022

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Wind power represents a promising source of renewable energies. Precise forecasting of wind power generation is crucial to mitigate the challenges of balancing supply and demand in the smart grid. Nevertheless, the major difficulty in wind power is its high fluctuation and intermittent nature, making it challenging to forecast. This study aims to develop efficient data-driven models to accurately forecast wind power generation. Crucially, the main contributions of this work are listed in the following major elements. Firstly, we investigate the performance of enhanced machine learning models to forecast univariate wind power time-series data. Specifically, we employed Bayesian optimization (BO) to optimally tune hyperparameters of the Gaussian process regression (GPR), Support Vector Regression (SVR) with different kernels, and ensemble learning (ES) models (i.e., Boosted trees and Bagged trees) and investigated their forecasting performance. Secondly, dynamic information has been incorporated in their construction to further enhance the forecasting performance of the investigated models. Specifically, we introduce lagged measurements to enable capturing time evolution into the design of the considered models. Furthermore, more input variables (e.g., wind speed and wind direction) are used to further improve wind prediction performance. Actual measurements from three wind turbines in France, Turkey, and Kaggle are used to verify the efficiency of the considered models. The results reveal the benefit of considering lagged data and input variables to better forecast wind power. The results also showed that the optimized GPR and ensemble models outperformed the other machine learning models.

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A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements

Cited by 18 publications

References 80 publications

Data quality analysis after hyperspectral LiDAR sequentially mapping trees

Data quality analysis after hyperspectral LiDAR sequentially mapping trees

Reliability of onshore wind turbines based on linking power curves to failure and maintenance records: A case study in central Spain

Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study

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