Ice prediction for wind turbine rotor blades with time series data and a deep learning approach

Kreutz, Markus; Alla, Abderrahim Ait; Lütjen, Michael; Ohlendorf, Jan-Hendrik; Freitag, Michael; Thoben, Klaus‐Dieter; Zimnol, Florian; Greulich, Andreas

doi:10.1016/j.coldregions.2022.103741

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…Unfortunately, such parameters are not always measured in a standard way for icing risk assessment, especially in older-generation wind turbines (which may or may not have sensors for detecting such external parameters). Nevertheless, it may be possible to use weather forecasting models to derive an approximation of these values (Fikke et al, 2007; Kreutz et al, 2023). However, dependence on approximations derived from such weather forecasting models (often based on numerical simulations) makes the operations and maintenance (O&M) process more complex, expensive, and not entirely reliable.…”

Section: Related Workmentioning

confidence: 99%

Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning

Chatterjee

Nieto

Gelbhardt

et al. 2023

Environ. Data Science

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Wind energy’s ability to liberate the world from conventional sources of energy relies on lowering the significant costs associated with the maintenance of wind turbines. Since icing events on turbine rotor blades are a leading cause of operational failures, identifying icing in advance is critical. Some recent studies have utilized deep learning (DL) techniques to predict icing events with high accuracy by leveraging rotor blade images, but these studies only focus on specific wind parks and fail to generalize to unseen scenarios (e.g., new rotor blade designs). In this paper, we aim to facilitate ice prediction on the face of lack of ice images in new wind parks. We propose the utilization of synthetic data augmentation via a generative artificial intelligence technique—the neural style transfer algorithm to improve the generalization of existing ice prediction models. We also compare the proposed technique with the CycleGAN as a baseline. We show that training standalone DL models with augmented data that captures domain-invariant icing characteristics can help improve predictive performance across multiple wind parks. Through efficient identification of icing, this study can support preventive maintenance of wind energy sources by making them more reliable toward tackling climate change.

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Section: Related Workmentioning

confidence: 99%

Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning

Chatterjee

Nieto

Gelbhardt

et al. 2023

Environ. Data Science

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“…et al proposed the BIKICE model, which utilizes time-series data and employs 5-fold crossvalidation methods to conduct ice detection on wind turbine rotor blades. This approach significantly improves accuracy [18]. Xu et al conducted an analysis of the environmental meteorological conditions, wind turbine operations, and power supply situations in a wind farm through field observations and numerical model predictions.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Turbine Blade Icing Wind Power Prediction Based on PCA-fLsm

Cai,

Jiang,

Song

et al. 2024

Energies

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To enhance the economic viability of wind energy in cold regions and ensure the safe operational management of wind farms, this paper proposes a short-term wind turbine blade icing wind power prediction method that combines principal component analysis (PCA) and fractional Lévy stable motion (fLsm). By applying supervisory control and data acquisition (SCADA) data from wind turbines experiencing icing in a mountainous area of Yunnan Province, China, the model comprehensively considers long-range dependence (LRD) and self-similar features. Adopting a combined pattern of previous-day predictions and actual measurement data, the model predicts the power under near-icing conditions, thereby enhancing the credibility and accuracy of icing forecasts. After validation and comparison with other prediction models (fBm, CNN-Attention-GRU, XGBoost), the model demonstrates a remarkable advantage in accuracy, achieving an accuracy rate and F1 score of 96.86% and 97.13%, respectively. This study proves the feasibility and wide applicability of the proposed model, providing robust data support for reducing wind turbine efficiency losses and minimizing operational risks.

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“…Therefore, engineers in the development phase must have credible thermal properties so that the effects on performance due to water freezing can be predicted and nullified. This is particularly important in aviation [21], road traffic [22], and energy production and transport, such as wind turbines [23] and transmission lines [24]. The freezing problem also appears in newer-energy power devices, such as proton exchange membrane fuel cells (PEMFC) [25].…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Thermal Properties of Frozen Tap, Demineralized, and Sea Water

Bošnjak,

Jurčević,

Bodrožić Ćoko

et al. 2023

Energies

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This paper reports an experimental investigation of the thermal properties of frozen tap, demineralized, and sea water. The presented research assists in a better understanding of the thermal properties of ice and the processes within it and contributes regarding the generation of novel experimental data. The thermal conductivity was measured in a range from −14 °C to −33 °C using the Transient Plane Source (TPS) method. Ice blocks were placed in an expanded polystyrene box in the freezer, which is where the measurements took place. The thermal conductivity of the tap water ice was observed to vary in a range from 1.915 ± 0.005 Wm−1K−1 at −14 °C to 2.060 ± 0.004 Wm−1K−1 at −33 °C. The values obtained for the ice made of demineralized water differed by less than 10%. The thermal conductivity of the sea ice was shown to be more temperature dependent, with the values ranging from 1.262 ± 0.005 Wm−1K−1 at −14 °C to 1.970 Wm−1K−1 ± 0.004 at −33 °C. A noticeable fall in the thermal conductivity of the sea ice was observed in the temperature range from −26 °C to −19 °C. A possible reason for this could be the increased precipitation of salt in that temperature range. Measurements of thermal diffusivity displayed similar trends as those of thermal conductivity. Specific volumetric heat capacity was indirectly calculated.

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Ice prediction for wind turbine rotor blades with time series data and a deep learning approach

Cited by 7 publications

References 30 publications

Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning

Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning

Short-Term Wind Turbine Blade Icing Wind Power Prediction Based on PCA-fLsm

Experimental Investigation of Thermal Properties of Frozen Tap, Demineralized, and Sea Water

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