scholarly journals Development of a wind turbine gearbox virtual load sensor using multibody simulation and artificial neural networks

2021 ◽  
Author(s):  
Baher Azzam ◽  
Ralf Schelenz ◽  
Björn Roscher ◽  
Abdul Baseer ◽  
Georg Jacobs
Keyword(s):  
Time Series ◽  
Wind Turbine ◽  
High Speed ◽  
Time Series Data ◽  
Development Trend ◽  
Series Data ◽  
Ann Model ◽  
Wind Turbine Gearbox ◽  
Virtual Sensor ◽  
Artificial Neural

AbstractA current development trend in wind energy is characterized by the installation of wind turbines (WT) with increasing rated power output. Higher towers and larger rotor diameters increase rated power leading to an intensification of the load situation on the drive train and the main gearbox. However, current main gearbox condition monitoring systems (CMS) do not record the 6‑degree of freedom (6-DOF) input loads to the transmission as it is too expensive. Therefore, this investigation aims to present an approach to develop and validate a low-cost virtual sensor for measuring the input loads of a WT main gearbox. A prototype of the virtual sensor system was developed in a virtual environment using a multi-body simulation (MBS) model of a WT drivetrain and artificial neural network (ANN) models. Simulated wind fields according to IEC 61400‑1 covering a variety of wind speeds were generated and applied to a MBS model of a Vestas V52 wind turbine. The turbine contains a high-speed drivetrain with 4‑points bearing suspension, a common drivetrain configuration. The simulation was used to generate time-series data of the target and input parameters for the virtual sensor algorithm, an ANN model. After the ANN was trained using the time-series data collected from the MBS, the developed virtual sensor algorithm was tested by comparing the estimated 6‑DOF transmission input loads from the ANN to the simulated 6‑DOF transmission input loads from the MBS. The results show high potential for virtual sensing 6‑DOF wind turbine transmission input loads using the presented method.

scholarly journals A novel cross-validation strategy for artificial neural networks using distributed-lag environmental factors

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244094
Author(s):  
Chao-Yu Guo ◽  
Tse-Wei Liu ◽  
Yi-Hau Chen
Keyword(s):  
Time Series ◽  
Cross Validation ◽  
Time Series Data ◽  
Series Data ◽  
Prediction Errors ◽  
Statistical Regression ◽  
Ann Model ◽  
Distributed Lag ◽  
Minimum Number ◽  
Artificial Neural

In recent years, machine learning methods have been applied to various prediction scenarios in time-series data. However, some processing procedures such as cross-validation (CV) that rearrange the order of the longitudinal data might ruin the seriality and lead to a potentially biased outcome. Regarding this issue, a recent study investigated how different types of CV methods influence the predictive errors in conventional time-series data. Here, we examine a more complex distributed lag nonlinear model (DLNM), which has been widely used to assess the cumulative impacts of past exposures on the current health outcome. This research extends the DLNM into an artificial neural network (ANN) and investigates how the ANN model reacts to various CV schemes that result in different predictive biases. We also propose a newly designed permutation ratio to evaluate the performance of the CV in the ANN. This ratio mimics the concept of the R-square in conventional statistical regression models. The results show that as the complexity of the ANN increases, the predicted outcome becomes more stable, and the bias shows a decreasing trend. Among the different settings of hyperparameters, the novel strategy, Leave One Block Out Cross-Validation (LOBO-CV), demonstrated much better results, and the lowest mean square error was observed. The hyperparameters of the ANN trained by the LOBO-CV yielded the minimum number of prediction errors. The newly proposed permutation ratio indicates that LOBO-CV can contribute up to 34% of the prediction accuracy.

Dynamic Predictions from Time Series Data — An Artificial Neural Network Approach

1997 ◽  
Vol 08 (06) ◽  
pp. 1345-1360 ◽  
Author(s):  
D. R. Kulkarni ◽  
J. C. Parikh ◽  
A. S. Pandya
Keyword(s):  
Time Series ◽  
Dynamic Systems ◽  
Time Series Data ◽  
Hybrid Approach ◽  
Computer Experiments ◽  
Series Data ◽  
Optimal Size ◽  
Good Prediction ◽  
Ann Model ◽  
Artificial Neural

A hybrid approach, incorporating concepts of nonlinear dynamics in artificial neural networks (ANN), is proposed to model a time series generated by complex dynamic systems. We introduce well-known features used in the study of dynamic systems — time delay τ and embedding dimension d — for ANN modeling of time series. These features provide a theoretical basis for selecting the optimal size for the number of neurons in the input layer. The main outcome of the new approach for such problems is that to a large extent it defines the ANN architecture, models the time series and gives good prediction. As a consequence, we have an integrated and systematic data-driven scheme for modeling time series data. We illustrate our method by considering computer generated periodic and chaotic time series. The ANN model developed gave excellent quality of fit for the training and test sets as well as for iterative dynamic predictions for future values of the two time series. Further, computer experiments were conducted by introducing Gaussian noise of various degrees in the two time series, to simulate real world effects. We find that up to a limit introduction of noise leads to a smaller network with good generalizing capability.

scholarly journals An Empirical Analysis of Energy Consumption, FDI and High Quality Development Based on Time Series Data of Zhejiang Province

2020 ◽  
Vol 17 (9) ◽  
pp. 3321
Author(s):  
Shaolong Zeng ◽  
Yiqun Liu ◽  
Junjie Ding ◽  
Danlu Xu
Keyword(s):  
Time Series ◽  
Economic Development ◽  
Energy Consumption ◽  
Open Economy ◽  
High Speed ◽  
Time Series Data ◽  
Rapid Development ◽  
Series Data ◽  
High Quality ◽  
The Relationship

This paper aims to identify the relationship among energy consumption, FDI, and economic development in China from 1993 to 2017, taking Zhejiang as an example. FDI is the main factor of the rapid development of Zhejiang’s open economy, which promotes the development of the economy, but also leads to the growth in energy consumption. Based on the time series data of energy consumption, FDI inflow, and GDP in Zhejiang from 1993 to 2017, we choose the vector auto-regression (VAR) model and try to identify the relationship among energy consumption, FDI, and economic development. The results indicate that there is a long-run equilibrium relationship among them. The FDI inflow promotes energy consumption, and the energy consumption promotes FDI inflow in turn. FDI promotes economic growth indirectly through energy consumption. Therefore, improving the quality of FDI and energy efficiency has become an inevitable choice to achieve the transition of Zhejiang’s economy from high speed growth to high quality growth.

scholarly journals Prediction of dynamic responses of floating structures using NARX with mirroring technique

2018 ◽  
Vol 203 ◽  
pp. 01025
Author(s):  
Ruly Irawan ◽  
Mohd Shahir Liew ◽  
Montasir Osman Ahmed Ali ◽  
Ahmad Mohamad Al Yacouby
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Dynamic Network ◽  
Dynamic Responses ◽  
Series Data ◽  
Ann Model ◽  
Floating Structure ◽  
Floating Structures ◽  
Time Series Modelling ◽  
Mirroring Technique

Displacements, velocities and accelerations of Six Degree of freedom of a single floating structure was predicted using Time Series NARX feedback neural Networks. The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic network, with feedback connections enclosing several layers of the network is based on the linear ARX model, which is commonly used in time-series modelling is used in this study. Time series data of displacements of a single floating structure was used for training and testing the ANN model. In the training stage, this time series data of environment parameters was used as input and dynamic responses was used as target. Benchmarking result and error prediction was compared between two techniques of Neural Network training. The prediction result of the model responses can be concluded that NARX with mirroring technique increase the accuracy and can be used to predict time series of dynamic responses of floating structures.

scholarly journals Spatio-temporal prediction of suspended sediment concentration in the coastal zone using an artificial neural network and a numerical model

2012 ◽  
Vol 14 (3) ◽  
pp. 574-584 ◽  
Author(s):  
B. Bhattacharya ◽  
T. van Kessel ◽  
D. P. Solomatine
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Numerical Model ◽  
Series Data ◽  
Ann Model ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Temporal Prediction ◽  
Artificial Neural

A problem of predicting suspended particulate matter (SPM) concentration on the basis of wind and wave measurements and estimates of bed shear stress done by a numerical model is considered. Data at a location at 10 km offshore from Noordwijk in the Dutch coastal area is used. The time series data have been filtered with a low pass filter to remove short-term fluctuations due to noise and tides and the resulting time series have been used to build an artificial neural network (ANN) model. The accuracy of the ANN model during both storm and calm periods was found to be high. The possibilities to apply the trained ANN model at other locations, where the model is assisted by the correctors based on the ratio of long-term average SPM values for the considered location to that for Noordwijk (for which the model was trained), have been investigated. These experiments demonstrated that the ANN model's accuracy at the other locations was acceptable, which shows the potential of the considered approach.

scholarly journals PRAKIRAAN HARGA AKARWANGI: APLIKASI METODE JARINGAN SYARAF TIRUAN

2020 ◽  
Vol 13 (1) ◽  
pp. 14
Author(s):  
CHANDRA INDRAWANTO ◽  
ERIYATNO ERIYATNO ◽  
ANAS M. FAUZI ◽  
MACHFUD MACHFUD ◽  
SUKARDI SUKARDI ◽  
...  
Keyword(s):  
Time Series ◽  
Key Words ◽  
Time Series Data ◽  
Oil Prices ◽  
Series Data ◽  
Vetiveria Zizanioides ◽  
Output Layer ◽  
Network Method ◽  
Artificial Neural ◽  
Hidden Layer

ABSTRAK<br />Prakiraan harga terna akarwangi dan harga minyak akarwangi telah<br />dilakukan dengan menggunakan metode jaringan syaraf tiruan. Memakai<br />data harga dari Januari 2000 sampai Agustus 2006 dilakukan prakiraan<br />harga untuk 24 bulan kedepan. Prakiraan terbaik dengan Mse pelatihan<br />dan Mse testing yang rendah didapat pada kombinasi fungsi aktivasi layar<br />tersembunyi sigmoid biner dan fungsi aktivasi output sigmoid bipolar<br />dengan rentang data transformasi (0,1) untuk prakiraan harga terna<br />akarwangi. Sedangkan untuk prakiraan harga minyak akarwangi didapat<br />pada fungsi aktivasi layar tersembunyi sigmoid bipolar dan fungsi aktivasi<br />output sigmoid biner dengan rentang data (0,1). Hasil prakiraan harga<br />menunjukkan harga rata-rata terna akarwangi dan harga rata-rata minyak<br />akarwangi untuk tahun 2007 dan 2008 masih di atas harga titik impas<br />usahatani maupun usaha agroindustri minyak akarwangi.<br />Kata kunci : Akarwangi, Vetiveria zizanioides L., harga, prakiraan,<br />jaringan syaraf tiruan, Jawa Barat<br />ABSTRACT<br />Vetiver oil prices forecasting with artificial neural<br />network method<br />Vetiver and vetiver oil prices forecasting with artificial neural<br />network method has been done. Time series data from January 2000 to<br />August 2006 was used to forecast the prices for 24 months ahead. The best<br />result for forecasting of vetiver prices was gotten using sigmoid binary<br />activation in hidden layer, sigmoid bipolar activation in output layer and<br />transformation data spread (0,1). The best result for forecasting of vetiver<br />oil prices was gotten using sigmoid bipolar activation in hidden layer,<br />sigmoid binary activation in output layer and transformation data spread<br />(0,1). The result shows that the average forecasting prices of vetiver and<br />vetiver oil in 2007 and 2008 higher than the prices needed for vetiver<br />farming and vetiver oil agroindustry to reach break event point.<br />Key words: Vetiveria zizanioides L., prices, forecasting, artificial neural<br />network, West Jav

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