A novel hybrid model based on weather variables relationships improving applied for wind speed forecasting

Fotso, Hervice Roméo Fogno; Kazé, Claude Vidal Aloyem; Kenmoé, G. Djuidjé

doi:10.1007/s40095-021-00408-x

Cited by 12 publications

(5 citation statements)

References 39 publications

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“…These methods have been previously used for solar radiation prediction and achieved satisfactory performance. Their usage has been validated through studies conducted for different locations and types of solar radiation, solar energy, and wind speed, proving these machine learning methods to be reliable and versatile [45]- [47]. Statistical methods are widely used for prediction in renewable energy systems, encompassing various approaches ranging from classical regression methods to deep learning methods [48].…”

Section: Methodsmentioning

confidence: 99%

Fatigue Analysis of Leaf Springs

Erkman

2023

Interdisciplinary Studies on Contemporary Research Practices in Engineering in the 21st Century-Iii

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This study aims to design leaf springs, also known as scissor springs, with the help of SolidWorks 2018 program in computer environment and then analyze them with the help of Ansys Workbench 18.1 program in computer environment. In this study, we designed the parabolic leaf spring sample, which was actually produced (Type - 1), in a computer environment and then made the necessary examinations by performing finite element analysis. In addition, in order to see the difference more clearly, the analysis was made with the modified version of the sample (Type - 2). The investigations are also written in sub-headings. The main purpose of this study is to design a leaf spring in a short period of time and to start mass production of leaf springs without the need for physical tests.

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Section: Methodsmentioning

confidence: 99%

Fatigue Analysis of Leaf Springs

Erkman

2023

Interdisciplinary Studies on Contemporary Research Practices in Engineering in the 21st Century-Iii

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“…Most of the works regarding machine learning techniques and wind power focus on the operational phase of the wind turbines. Mainly, three research lines can be outlined: the prediction of the electrical output, which can be based only on historical data [16], on wind velocity records [17,18]-which can also be predicted using Machine Learning and Artificial Intelligence techniques as hybrid models [19], fuzzy logic [20], Deep Learning [21] or ensemble methods [22]-or on multiple environmental variables [23,24]; the creation of assistant systems for the design and control of wind turbines [25] and wind farms [26][27][28]; and the development of smart and knowledge-based maintenance system for the wind turbines, mainly focused towards fault classification [29,30], anomaly detection [31] and remaining useful life (RUL) estimation [32][33][34]. For an exhaustive review of the machine-learningbased approaches to wind turbine condition monitoring, see Ref.…”

Section: Machine Learning and Wind Turbine Tower Manufacturingmentioning

confidence: 99%

Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study

et al. 2022

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This study analyzes the lead time of the bending operation in the wind turbine tower manufacturing process. Since the operation involves a significant amount of employee interaction and the parts processed are heavy and voluminous, there is considerable variability in the recorded lead times. Therefore, a machine learning regression analysis has been applied to the bending process. Two machine learning algorithms have been used: a multivariate Linear Regression and the M5P method. The goal of the analysis is to gain a better understanding of the effect of several factors (technical, organizational, and experience-related) on the bending process times, and to attempt to predict these operation times as a way to increase the planning and controlling capacity of the plant. The inclusion of the experience-related variables serves as a basis for analyzing the impact of age and experience on the time-wise efficiency of workers. The proposed approach has been applied to the case of a Spanish wind turbine tower manufacturer, using data from the operation of its plant gathered between 2018 and 2021. The results show that the trained models have a moderate predictive power. Additionally, as shown by the output of the regression analysis, there are variables that would presumably have a significant impact on lead times that have been found to be non-factors, as well as some variables that generate an unexpected degree of variability.

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“…The PMSG still has some disadvantages, notably the need for a flexibly structured electromagnetic field, which entails high production and operating standards [7]. In addition, variable-speed operation of the generator requires the use of a power inverter, which plays an important role in improving the performance of the wind energy conversion system (WECS) [8,9].…”

Section: Introductionmentioning

confidence: 99%

Control of turbine speed based Genetic Algorithm Regulator for the wind speed connected to PMSG

2023

Proceeding Book of 3rd International Conference on Scientific and Academic Research ICSAR 2023

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This paper proposes a control strategy for a wind energy conversion system equipped with a permanent magnet synchronous generator connected to the electrical grid. The controller parameters are synthesized using a genetic optimization algorithm. This control strategy is validated by numerical simulations at different wind speeds. The dynamic performance analysis proves the effectiveness of the control adopted, in fact, a good robustness. In addition, the performance of the mechanical turbine speed response obtained with a speed controller based on the genetic algorithm has been compared with that obtained with a conventional PI_controller. The efficiency of the structure under consideration is validated using MATLAB Simulink simulations .

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A novel hybrid model based on weather variables relationships improving applied for wind speed forecasting

Cited by 12 publications

References 39 publications

Fatigue Analysis of Leaf Springs

Fatigue Analysis of Leaf Springs

Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study

Control of turbine speed based Genetic Algorithm Regulator for the wind speed connected to PMSG

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