Dimensionality Reduction for the Analysis of Time Series Data from Wind Turbines

Garcke, Jochen; Iza-Teran, Rodrigo; Marks, Marvin; Pathare, Mandar; Schollbach, Dirk; Stettner, M.

doi:10.1007/978-3-319-62458-7_16

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…In Dervilis et al 20 the authors applied Autoencoders (also commonly referred to as “Auto‐associators”) with radial basis functions and PCA for demonstrating the utility of pattern recognition in damage detection on a wind turbine blade tested in the lab. In Garcke et al, 21 the authors demonstrated how classical non‐linear dimensionality reduction methods, such as Dynamic Time Warping 22 and diffusion maps 23 can be used to extract insights from simulated time‐series of wind turbine responses. In Tang et al, 24 the authors used a particular dimensionality reduction technique, namely, Orthogonal Neighborhood Preserving Embedding 25 for training a support vector machine classifier.…”

Section: Prior Related Work On Machine Learning and Probabilistic Techniques For Damage Monitoring And Remaining Useful Life Predictionmentioning

confidence: 99%

Conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data

2021

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Wind turbine fatigue estimation is based on time‐consuming Monte Carlo simulations for various wind conditions, followed by cycle‐counting procedures and the application of engineering damage models. The outputs of the fatigue simulations are large in volume and of high dimensionality, as they typically consist of estimates on finite‐element computational meshes. The strain and stress tensor time series, which are the primary quantities of interest when considering the problem of fatigue estimation, are dictated by complex vibration characteristics due to the coupled effect of aerodynamics, structural dynamics, geometrically non‐linear mechanics, and control. A Variational Auto‐Encoder (VAE) is trained in order to model the probability distribution of the accumulated fatigue on the root cross‐section of a simulated wind turbine blade. The VAE is conditioned on historical data that correspond to coarse wind‐field measurement statistics, such as mean hub‐height wind speed, standard deviation of hub‐height wind speed and shear exponent. In the absence of direct measurements of structural loads, the proposed technique finds applications in making long‐term probabilistic deterioration predictions from historical Supervisory, Control, and Data Acquisition (SCADA) data, while capturing the inherent aleatoric uncertainty due to the incomplete information on strain time series of the wind turbine structure, when only SCADA data statistics are available.

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Section: Prior Related Work On Machine Learning and Probabilistic Techniques For Damage Monitoring And Remaining Useful Life Predictionmentioning

confidence: 99%

Conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data

2021

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“…For rapid clustering and dimension reduction, the pattern and seasonality of time series data structure are seen by computational data values such as Principal Component Analysis (PCA), wavelets, etc. Some techniques are used to minimize the dimensionality of time series clustering (Garcke et al, 2017). By using the seasonal structure and trend values, clustering helps us to find a similar group of time-series data.…”

Section: Introductionmentioning

confidence: 99%

A novel time series data clustering approach for wind speed forecasting

2022

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Wind energy plays an essential role in the generation process of sustainable energy, with a bright future. Therefore, predicting wind speed fluctuations and their output power plays a crucial role in electric power generation. The integration of wind power is based on the accuracy of wind speed and power prediction model. In this paper, a clustering algorithm is proposed based on the length of the trendlet components. After spotting the different clusters, one suitable cluster is selected for modeling using the panda’s correlation method. This paper uses specific ARIMA, Naive Forecast, and Holt Winter models to forecast the selected cluster. Here three hybrid models, namely, C-ARIMA, C-NAIVE Forecast, and C-Holt-Winter, are proposed for wind speed forecasting. The performances of the proposed models are evaluated using the mean absolute error (MAE) and root mean squared error (RMSE). The experiment outcomes show that the cluster-based forecasting technique (Hybrid models) improved performance compared with un-clustered forecasting techniques.

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“…It is essential to convert the data into a meaningful form for accurate data analysis, which requires pre-processing the data before it can be used to develop a prediction or classification model. To improve classification accuracy, dimensional reduction [ 25 , 26 , 27 ] and data augmentation [ 28 , 29 , 30 ] have been studied. Garcke et al [ 25 ] proposed a method to reduce the dimension of nonlinear time-series data extracted from wind turbines, setting the baseline so as to distinguish normal turbines from abnormal turbines, and monitoring the state of the wind turbines.…”

Section: Introductionmentioning

confidence: 99%

“…To improve classification accuracy, dimensional reduction [ 25 , 26 , 27 ] and data augmentation [ 28 , 29 , 30 ] have been studied. Garcke et al [ 25 ] proposed a method to reduce the dimension of nonlinear time-series data extracted from wind turbines, setting the baseline so as to distinguish normal turbines from abnormal turbines, and monitoring the state of the wind turbines. In order to solve the multidimensional problem presented by time-series data acquired from a virtual sensor, dimension reduction was performed.…”

Section: Introductionmentioning

confidence: 99%

Resource-Efficient Pet Dog Sound Events Classification Using LSTM-FCN Based on Time-Series Data

Kim

Chung

et al. 2018

Sensors

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The use of IoT (Internet of Things) technology for the management of pet dogs left alone at home is increasing. This includes tasks such as automatic feeding, operation of play equipment, and location detection. Classification of the vocalizations of pet dogs using information from a sound sensor is an important method to analyze the behavior or emotions of dogs that are left alone. These sounds should be acquired by attaching the IoT sound sensor to the dog, and then classifying the sound events (e.g., barking, growling, howling, and whining). However, sound sensors tend to transmit large amounts of data and consume considerable amounts of power, which presents issues in the case of resource-constrained IoT sensor devices. In this paper, we propose a way to classify pet dog sound events and improve resource efficiency without significant degradation of accuracy. To achieve this, we only acquire the intensity data of sounds by using a relatively resource-efficient noise sensor. This presents issues as well, since it is difficult to achieve sufficient classification accuracy using only intensity data due to the loss of information from the sound events. To address this problem and avoid significant degradation of classification accuracy, we apply long short-term memory-fully convolutional network (LSTM-FCN), which is a deep learning method, to analyze time-series data, and exploit bicubic interpolation. Based on experimental results, the proposed method based on noise sensors (i.e., Shapelet and LSTM-FCN for time-series) was found to improve energy efficiency by 10 times without significant degradation of accuracy compared to typical methods based on sound sensors (i.e., mel-frequency cepstrum coefficient (MFCC), spectrogram, and mel-spectrum for feature extraction, and support vector machine (SVM) and k-nearest neighbor (K-NN) for classification).

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Dimensionality Reduction for the Analysis of Time Series Data from Wind Turbines

Cited by 5 publications

References 12 publications

Conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data

Conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data

A novel time series data clustering approach for wind speed forecasting

Resource-Efficient Pet Dog Sound Events Classification Using LSTM-FCN Based on Time-Series Data

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