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 9 publications

(4 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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“…from wind energy with the help of a generator and other accessories. But, the wind energy is mostly unpredictable due to intermittent nature of wind speed [6]. Considering the unsettling nature of the wind speed, WTs are broadly categorised into two types, e.g.…”

Section: Introductionmentioning

confidence: 99%

Protecting DFIG‐based multi‐machine power system under transient‐state by nonlinear adaptive backstepping controller‐based capacitive BFCL

Hossain

Islam

Haque

et al. 2022

IET Generation Trans & Dist

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The invention of doubly‐fed induction generator (DFIG) brings the wind energy one step ahead as renewable power generation. But the performance of the grid‐connected DFIGs are greatly affected by grid disturbances as their stator windings are interfaced to the grid directly. Different fault current limiters are capable of improving fault ride through capability during short circuit faults. Nonlinear controller based fault current limiters (FCLs) are superior to deal with the nonlinearity of the power systems. This paper proposes a nonlinear adaptive backstepping controller (ABSC) based capacitive bridge‐type FCL (CBFCL) to enhance the fault ride through capability of a DFIG‐based wind farm connected to a multi‐machine power system. At first, a complete modelling of the CBFCL is derived to understand its behaviour during the normal and fault period more accurately. Then, the ABSC is designed based on that dynamic model, along with the backstepping controller (BSC) and sliding mode controller (SMC) for comparison purpose. Finally, the performance of the ABSC to control the CBFCL has been analysed and verified by comparing with that of the BSC and the sliding mode controller. All the graphical and mathematical analyses favour the ABSC based CBFCL under symmetrical and asymmetrical fault (both temporary and permanent) scenarios.

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

Cited by 9 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

Protecting DFIG‐based multi‐machine power system under transient‐state by nonlinear adaptive backstepping controller‐based capacitive BFCL

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