scholarly journals Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7262
Author(s):  
Gregory Duthé ◽  
Imad Abdallah ◽  
Sarah Barber ◽  
Eleni Chatzi
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Dynamic Response ◽  
Wind Turbine ◽  
Leading Edge ◽  
Surface Erosion ◽  
Maintenance Costs ◽  
Degradation Processes ◽  
Proposed Model ◽  
Spatio Temporal

Leading edge surface erosion is an emerging issue in wind turbine blade reliability, causing a reduction in power performance, aerodynamic loads imbalance, increased noise emission, and, ultimately, additional maintenance costs, and, if left untreated, it leads to the compromise of the functionality of the blade. In this work, we first propose an empirical spatio-temporal stochastic model for simulating leading edge erosion, to be used in conjunction with aeroelastic simulations, and subsequently present a deep learning model to be trained on simulated data, which aims to monitor leading edge erosion by detecting and classifying the degradation severity. This could help wind farm operators to reduce maintenance costs by planning cleaning and repair activities more efficiently. The main ingredients of the model include a damage process that progresses at random times, across multiple discrete states characterized by a non-homogeneous compound Poisson process, which is used to describe the random and time-dependent degradation of the blade surface, thus implicitly affecting its aerodynamic properties. The model allows for one, or more, zones along the span of the blades to be independently affected by erosion. The proposed model accounts for uncertainties in the local airfoil aerodynamics via parameterization of the lift and drag coefficients’ curves. The proposed model was used to generate a stochastic ensemble of degrading airfoil aerodynamic polars, for use in forward aero-servo-elastic simulations, where we computed the effect of leading edge erosion degradation on the dynamic response of a wind turbine under varying turbulent input inflow conditions. The dynamic response was chosen as a defining output as this relates to the output variable that is most commonly monitored under a structural health monitoring (SHM) regime. In this context, we further proposed an approach for spatio-temporal dependent diagnostics of leading erosion, namely, a deep learning attention-based Transformer, which we modified for classification tasks on slow degradation processes with long sequence multivariate time-series as inputs. We performed multiple sets of numerical experiments, aiming to evaluate the Transformer for diagnostics and assess its limitations. The results revealed Transformers as a potent method for diagnosis of such degradation processes. The attention-based mechanism allows the network to focus on different features at different time intervals for better prediction accuracy, especially for long time-series sequences representing a slow degradation process.

scholarly journals Modelling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades

2021 ◽  
Author(s):  
Gregory Duthé ◽  
Imad Abdallah ◽  
Sarah Barber ◽  
Eleni Chatzi
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Dynamic Response ◽  
Wind Turbine ◽  
Leading Edge ◽  
Surface Erosion ◽  
Degradation Processes ◽  
Aerodynamic Properties ◽  
Proposed Model ◽  
Spatio Temporal

Leading edge surface erosion is an emerging issue in wind turbine blade reliability, causing reduction in power performance, aerodynamic loads imbalance, increased noise emission and ultimately additional maintenance costs, and if left untreated, leads to the compromise of the functionality of the blade. In this work, we first propose an empirical spatio-temporal stochastic model for simulating leading edge erosion, to be used in conjunction with aeroelastic simulations, and subsequently propose a deep learning model trained on simulated data, which aims to monitor leading edge erosion by detecting and classifying the degradation severity. The main ingredients of the model include a damage process that progresses at random times, across multiple discrete states characterized by a non-homogeneous compound Poisson process, which is used to describe the random and time-dependent degradation of the blade surface, thus implicitly affecting its aerodynamic properties. The model allows for one, or more, zones along the span of the blades to be independently affected by erosion. The proposed model accounts for uncertainties in the local airfoil aerodynamics via parameterization of the lift and drag coefficients curves. The proposed model is used to generate a stochastic ensemble of degrading airfoil aerodynamic polars, for use in forward aero-servo-elastic simulations, where we compute the effect of leading edge erosion degradation on the dynamic response of a wind turbine under varying turbulent input inflow conditions. The dynamic response is chosen a defining output as this relates to the output variable that is most commonly monitored under a Structural Health Monitoring (SHM) regime. In this context, we further propose an approach for spatio-temporal dependent diagnostics of leading erosion, namely, a deep learning attention-based Transformer, which we modify for classification tasks on slow degradation processes with long sequence multivariate time-series as inputs. We perform multiple sets of numerical experiments, aiming to evaluate the Transformer for diagnostics and assess its limitations. The results reveal Transformers as a potent method for diagnosis of such degradation processes. The attention-based mechanism allows the network to focus on different features at different time intervals for better prediction accuracy, especially for long time-series sequences representing a slow degradation process.

scholarly journals Brief communication: Nowcasting of precipitation for leading-edge-erosion-safe mode

2020 ◽  
Vol 5 (3) ◽  
pp. 977-981 ◽  
Author(s):  
Anna-Maria Tilg ◽  
Charlotte Bay Hasager ◽  
Hans-Jürgen Kirtzel ◽  
Poul Hummelshøj
Keyword(s):  
Liquid Phase ◽  
Wind Turbine ◽  
Turbine Blades ◽  
Leading Edge ◽  
Wind Turbine Blades ◽  
Spatio Temporal ◽  
The Impact ◽  
Theoretical Considerations ◽  
Mode A ◽  
Precipitation Events

Abstract. Leading-edge erosion (LEE) of wind turbine blades is caused by the impact of hydrometeors, which appear in a solid or liquid phase. A reduction in the wind turbine blades' tip speed during defined precipitation events can mitigate LEE. To apply such an erosion-safe mode, a precipitation nowcast is required. Theoretical considerations indicate that the time a raindrop needs to fall to the ground is sufficient to reduce the tip speed. Furthermore, it is described that a compact, vertically pointing radar that measures rain at different heights with a sufficiently high spatio-temporal resolution can nowcast rain for an erosion-safe mode.

scholarly journals A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

2020 ◽  
Vol 12 (8) ◽  
pp. 3103 ◽  
Author(s):  
Hyojoo Son ◽  
Changwan Kim
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Power System ◽  
Electricity Demand ◽  
Efficient Production ◽  
Power System Planning ◽  
System Planning ◽  
Residential Sector ◽  
Use Of Resources ◽  
Proposed Model

Forecasting electricity demand at the regional or national level is a key procedural element of power-system planning. However, achieving such objectives in the residential sector, the primary driver of peak demand, is challenging given this sector’s pattern of constantly fluctuating and gradually increasing energy usage. Although deep learning algorithms have recently yielded promising results in various time series analyses, their potential applicability to forecasting monthly residential electricity demand has yet to be fully explored. As such, this study proposed a forecasting model with social and weather-related variables by introducing long short-term memory (LSTM), which has been known to be powerful among deep learning-based approaches for time series forecasting. The validation of the proposed model was performed using a set of data spanning 22 years in South Korea. The resulting forecasting performance was evaluated on the basis of six performance measures. Further, this model’s performance was subjected to a comparison with the performance of four benchmark models. The performance of the proposed model was exceptional according to all of the measures employed. This model can facilitate improved decision-making regarding power-system planning by accurately forecasting the electricity demands of the residential sector, thereby contributing to the efficient production and use of resources.

scholarly journals Incomplete Label Multi-Task Deep Learning for Spatio-Temporal Event Subtype Forecasting

2019 ◽  
Vol 33 ◽  
pp. 3638-3646 ◽  
Author(s):  
Yuyang Gao ◽  
Liang Zhao ◽  
Lingfei Wu ◽  
Yanfang Ye ◽  
Hui Xiong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Spatial Heterogeneity ◽  
Complex Problem ◽  
Strongly Coupled ◽  
Social Risks ◽  
Proposed Model ◽  
Future Events ◽  
Spatio Temporal ◽  
Real World Datasets ◽  
Spatial Locations

Due to the potentially significant benefits for society, forecasting spatio-temporal societal events is currently attracting considerable attention from researchers. Beyond merely predicting the occurrence of future events, practitioners are now looking for information about specific subtypes of future events in order to allocate appropriate amounts and types of resources to manage such events and any associated social risks. However, forecasting event subtypes is far more complex than merely extending binary prediction to cover multiple classes, as 1) different locations require different models to handle their characteristic event subtype patterns due to spatial heterogeneity; 2) historically, many locations have only experienced a incomplete set of event subtypes, thus limiting the local model’s ability to predict previously “unseen” subtypes; and 3) the subtle discrepancy among different event subtypes requires more discriminative and profound representations of societal events. In order to address all these challenges concurrently, we propose a Spatial Incomplete Multi-task Deep leArning (SIMDA) framework that is capable of effectively forecasting the subtypes of future events. The new framework formulates spatial locations into tasks to handle spatial heterogeneity in event subtypes, and learns a joint deep representation of subtypes across tasks. Furthermore, based on the “first law of geography”, spatiallyclosed tasks share similar event subtype patterns such that adjacent tasks can share knowledge with each other effectively. Optimizing the proposed model amounts to a new nonconvex and strongly-coupled problem, we propose a new algorithm based on Alternating Direction Method of Multipliers (ADMM) that can decompose the complex problem into subproblems that can be solved efficiently. Extensive experiments on six real-world datasets demonstrate the effectiveness and efficiency of the proposed model.

scholarly journals Forecasting of Tomato Yields Using Attention-Based LSTM Network and ARMA Model

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1576
Author(s):  
Wanhyun Cho ◽  
Sangkyuoon Kim ◽  
Myunghwan Na ◽  
Inseop Na
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Environmental Factors ◽  
Environmental Variables ◽  
Time Series Data ◽  
Arma Model ◽  
Series Data ◽  
Proposed Model ◽  
Lstm Network ◽  
Statistical Time

Nonlinear autoregressive exogenous (NARX), autoregressive integrated moving average (ARIMA) and multi-layer perceptron (MLP) networks have been widely used to predict the appearance value of future points for time series data. However, in recent years, new approaches to predict time series data based on various networks of deep learning have been proposed. In this paper, we tried to predict how various environmental factors with time series information affect the yields of tomatoes by combining a traditional statistical time series model and a deep learning model. In the first half of the proposed model, we used an encoding attention-based long short-term memory (LSTM) network to identify environmental variables that affect the time series data for tomatoes yields. In the second half of the proposed model, we used the ARMA model as a statistical time series analysis model to improve the difference between the actual yields and the predicted yields given by the attention-based LSTM network at the first half of the proposed model. Next, we predicted the yields of tomatoes in the future based on the measured values of environmental variables given during the observed period using a model built by integrating the two models. Finally, the proposed model was applied to determine which environmental factors affect tomato production, and at the same time, an experiment was conducted to investigate how well the yields of tomatoes could be predicted. From the results of the experiments, it was found that the proposed method predicts the response value using exogenous variables more efficiently and better than the existing models. In addition, we found that the environmental factors that greatly affect the yields of tomatoes are internal temperature, internal humidity, and CO2 level.

scholarly journals An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 384
Author(s):  
Francisca Corpas-Burgos ◽  
Miguel A. Martinez-Beneito
Keyword(s):  
Time Series ◽  
Disease Mapping ◽  
Mortality Data ◽  
Data Sets ◽  
Modeling Tools ◽  
Polynomial Fit ◽  
Proposed Model ◽  
Temporal Models ◽  
Temporal Model ◽  
Spatio Temporal

One of the more evident uses of spatio-temporal disease mapping is forecasting the spatial distribution of diseases for the next few years following the end of the period of study. Spatio-temporal models rely on very different modeling tools (polynomial fit, splines, time series, etc.), which could show very different forecasting properties. In this paper, we introduce an enhancement of a previous autoregressive spatio-temporal model with particularly interesting forecasting properties, given its reliance on time series modeling. We include a common spatial component in that model and show how that component improves the previous model in several ways, its predictive capabilities being one of them. In this paper, we introduce and explore the theoretical properties of this model and compare them with those of the original autoregressive model. Moreover, we illustrate the benefits of this new model with the aid of a comprehensive study on 46 different mortality data sets in the Valencian Region (Spain) where the benefits of the new proposed model become evident.

scholarly journals Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks

2021 ◽  
Vol 18 (22) ◽  
pp. 12213
Author(s):  
Taesung Kim ◽  
Jinhee Kim ◽  
Wonho Yang ◽  
Hunjoo Lee ◽  
Jaegul Choo
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Air Quality ◽  
Time Series Data ◽  
Quality Data ◽  
Series Data ◽  
Missing Value ◽  
Missing Value Imputation ◽  
Spatio Temporal ◽  
Air Quality Data

To prevent severe air pollution, it is important to analyze time-series air quality data, but this is often challenging as the time-series data is usually partially missing, especially when it is collected from multiple locations simultaneously. To solve this problem, various deep-learning-based missing value imputation models have been proposed. However, often they are barely interpretable, which makes it difficult to analyze the imputed data. Thus, we propose a novel deep learning-based imputation model that achieves high interpretability as well as shows great performance in missing value imputation for spatio-temporal data. We verify the effectiveness of our method through quantitative and qualitative results on a publicly available air-quality dataset.

scholarly journals A Comprehensive Analysis of Wind Turbine Blade Damage

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5974
Author(s):  
Dimitris Al. Katsaprakakis ◽  
Nikos Papadakis ◽  
Ιοannis Ntintakis
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Turbine Blades ◽  
Leading Edge ◽  
Wind Turbine Blade ◽  
Surface Erosion ◽  
Wind Turbine Blades ◽  
Adequate Treatment ◽  
Effective Measure ◽  
Climate Conditions

The scope of this article is to review the potential causes that can lead to wind turbine blade failures, assess their significance to a turbine’s performance and secure operation and summarize the techniques proposed to prevent these failures and eliminate their consequences. Damage to wind turbine blades can be induced by lightning, fatigue loads, accumulation of icing on the blade surfaces and the exposure of blades to airborne particulates, causing so-called leading edge erosion. The above effects can lead to damage ranging from minor outer surface erosion to total destruction of the blade. All potential causes of damage to wind turbine blades strongly depend on the surrounding environment and climate conditions. Consequently, the selection of an installation site with favourable conditions is the most effective measure to minimize the possibility of blade damage. Otherwise, several techniques and methods have already been applied or are being developed to prevent blade damage, aiming to reduce damage risk if not able to eliminate it. The combined application of damage prevention strategies with a SCADA system is the optimal approach to adequate treatment.

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