Review of Data-Driven Approaches for Wind Turbine Blade Icing Detection

Cai, Chang; Guo, Jicai; Song, Xiaowen; Zhang, Yanfeng; Wu, Jianxin; Tang, Shufeng; Jia, Yan; Xing, Zhitai; Li, Qingan

doi:10.3390/su15021617

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…Monitoring wind turbine icing and its safety status is the basis of studying the icing. In order to achieve this goal, researchers have designed different detection systems or methods based on simulation and laboratory tests, and have carried out field verification [28][29][30]66,67]. The monitoring of blade icing status can be divided into direct monitoring and indirect monitoring.…”

Section: Icing Monitoring and Safety Status Assessmentmentioning

confidence: 99%

“…It has also analyzed the more cerning indicators: the icing influence on the output power of the turbines, and the ch in power curve with the icing degree [24][25][26][27]. A device and system for monitoring the th ness and state of turbine icing were developed and designed [28][29][30], as well as a consid tion for the economic benefits caused by turbine shutdown accidents [31][32][33]. Researc Any small change in the surface of the turbine blades can negatively influence the aerodynamic characteristics of the turbine, and severe icing events can cause the turbines to shut down completely.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Wind Turbine Icing and Anti/De-Icing Technologies

Zhang,

Zhang

et al. 2024

Energies

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The development and utilization of clean energy is becoming more extensive, and wind power generation is one of the key points of this. Occasionally, wind turbines are faced with various extreme environmental impacts such as icing, lightning strikes and so on. In particular, the icing of wind turbines increases icing–wind loads, and results in a reduced power output. And blades broken down lead to large-area shutdown accidents caused by high-speed rotating, which seriously affects the reliability and equipment safety of wind power generation. Relevant institutions and researchers at home and abroad have carried out a lot of research on this. This paper summarizes the formation and influencing factors of wind turbine icing, the influence of icing on wind power generation, and defense technologies. First, it introduces the formation conditions and mechanisms of icing in wind farm regions and the relationship between meteorological and climatic characteristics and icing, and analyzes the key influence factors on icing. Then, the impact of icing on wind turbines is explained from the aspects of mechanical operation, the power curve, jeopardies and economic benefits. And then the monitoring and safety status of wind turbines icing is analyzed, which involves collecting the relevant research on anti-de-icing in wind power generation, introducing various anti/de-icing technologies, and analyzing the principle of icing defense. Finally, this paper summarizes wind turbine icing and its defense technologies, and puts forward the future research direction based on the existing problems of wind power generation icing.

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Section: Icing Monitoring and Safety Status Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review of Wind Turbine Icing and Anti/De-Icing Technologies

Zhang,

Zhang

et al. 2024

Energies

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“…According to modeling theory, these studies can be classified into methods based on physical models and data-driven statistical methods. Among them, statistical methods based on machine learning (ML) can automatically mine the connections between data features [6,7], are simple to model, have fast calculation speed and high prediction accuracy, and have been widely used. Literature [8] selected key features according to the icing mechanism in extremely cold weather and used particle swarm optimization (PSO) optimized support vector machine (SVM) to predict the icing fault of wind turbine blades; Zhang et al [9] studied the use of monitoring and data acquisition system data to detect icing on wind turbine blades, and proposed a prediction model based on the random forest (RF) algorithm; The study fully considers the mixed characteristics of short-term and long-term icing effects based on the physical extraction of bottom icing, and uses these characteristics to establish a Stacked-extreme gradient boosting (XGBoost) model to realize leaf icing diagnosis [10]; Tang et al [11] proposed a fault detection model for doubly-fed wind turbine pitch system based on IHHO-light gradient boosting machine (LightGBM); literature [12] introduced a modeling method using weather research and forecasting models to predict the failure probability of wind turbines under typhoon weather.…”

Section: Introductionmentioning

confidence: 99%

A Fault and Capacity Loss Prediction Method of Wind Power Station under Extreme Weather

Li,

Zhuo,

Meng

et al. 2023

Mathematical Problems in Engineering

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Extreme weather events can severely affect the operation and power generation of wind farms and threaten the stability and safety of grids with high penetration of renewable energy. Therefore, it is crucial to forecast the failure and capacity loss of wind farms under extreme weather conditions. To this end, considering the disaster-causing mechanism of severe weather and the operational characteristics of wind farms, this paper first uses the density-based spatial clustering of applications with noise algorithm to cluster the units in the wind farm based on the operating characteristics affected by the weather, and uses correlation analysis methods to extract key disaster-causing factors in extreme weather; then proposes a prediction model based on feature-weighted stacking integration. The model adopts the stacking-integrated learning architecture to support multiple learners and performs feature weighting according to the prediction accuracy of each learner in the base learner, thereby improving the training effect of the meta-learner and improving the prediction accuracy of the model. The prediction model is used to predict each wind turbine group based on the extracted key features and to predict the failure and capacity loss of the wind farm. Finally, an example analysis is performed based on actual data from a wind farm, and the results show that the proposed prediction method can effectively predict the operational reliability of wind farms.

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Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults

Maestro-Prieto,

Ramírez-Sanz,

Bustillo

et al. 2024

Appl Intell

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Both wear-induced bearing failure and misalignment of the powertrain between the rotor and the electrical generator are common failure modes in wind-turbine motors. In this study, Semi-Supervised Learning (SSL) is applied to a fault detection and diagnosis solution. Firstly, a dataset is generated containing both normal operating patterns and seven different failure classes of the two aforementioned failure modes that vary in intensity. Several datasets are then generated, maintaining different numbers of labeled instances and unlabeling the others, in order to evaluate the number of labeled instances needed for the desired accuracy level. Subsequently, different types of SSL algorithms and combinations of algorithms are trained and then evaluated with the test data. The results showed that an SSL approach could improve the accuracy of trained classifiers when a small number of labeled instances were used together with many unlabeled instances to train a Co-Training algorithm or combinations of such algorithms. When a few labeled instances (fewer than 10% or 327 instances, in this case) were used together with unlabeled instances, the SSL algorithms outperformed the result obtained with the Supervised Learning (SL) techniques used as a benchmark. When the number of labeled instances was sufficient, the SL algorithm (using only labeled instances) performed better than the SSL algorithms (accuracy levels of 87.04% vs. 86.45%, when labeling 10% of instances). A competitive accuracy of 97.73% was achieved with the SL algorithm processing a subset of 40% of the labeled instances. Graphical abstract Steps and processes for approaching semi-supervised FDD of wind-turbine gearbox misalignment and imbalance faults

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Review of Data-Driven Approaches for Wind Turbine Blade Icing Detection

Cited by 5 publications

References 44 publications

A Review of Wind Turbine Icing and Anti/De-Icing Technologies

A Review of Wind Turbine Icing and Anti/De-Icing Technologies

A Fault and Capacity Loss Prediction Method of Wind Power Station under Extreme Weather

Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults

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