Design and Simulation of Wind Farm in Tarhuna Region with Economic Analysis

Mrehel, Omar Guma; Daw, Nouri Ali; Jbail, Abdalnasser Farj Ali

doi:10.1109/sta.2019.8717192

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…Over the past few decades, renewable energies have been widely used globally due to their beneficial criterion that helps humanity live in a better world [1][2][3][4][5][6][7][8][9][10]. The development of renewable energy techniques keeps improving to cope with the environmental requirements [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis

Shahrulhisham

Chong²,

Yaw³

et al. 2022

J. Phys.: Conf. Ser.

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Wind energies are one of the most used resources worldwide and favours the economy by not emitting harmful gases that could lead to global warming. It is a cost-efficient method and environmentally friendly. Hence, explains the popularity of wind energy production over the years. Unfortunately, a minor fault could be contagious by affecting the nearby components, then a more complicated problem might arise, which may be costly. Thus, this article conducted a machine learning technique, support vector machine (SVM) to monitor the health of the wind turbine system by classifying the class of healthy data and faulty data. Some SVM types were experimented with, including Linear, Quadratic, Cubic, Fine Gaussian, Medium Gaussian, and Coarse Gaussian. Then these models were trained under different validation schemes that are cross-validation, holdout validation, and re-substitution validation as an approach to evaluate the performance of each model. In the end, Cubic SVM is proven to outperformed other models under the provision of 10-fold cross-validation with an accuracy of 98.25%. The result showed that Cubic SVM has the best performance while Linear SVM has the least accuracy among other models. Hence choosing the default value is preferred as the final product to diagnose the fault in wind turbine systems.

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Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis

Shahrulhisham

Chong²,

Yaw³

et al. 2022

J. Phys.: Conf. Ser.

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“…The renewable energy (RE) has been increasingly used worldwide as the energy demand grows in parallel with the growth in population, urbanization and industrial development [1,2]. Wind energy is one of the fastest growing RE options [3][4][5][6][7][8][9][10][11][12], especially in Asia [13][14][15][16][17][18][19][20] and North America [3,[21][22][23][24][25], as some of the key advantages of wind energy include high availability, scale flexibility, no greenhouse gas (GHG) emission, low construction time and efficiency in energy generation [26]. In turn, installation of wind turbines (WT) has been on the rise with increasing installation capacity worldwide [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis in Wind Energy Management System using Extreme Learning Machine: A Systematic Review

Yaw

Teoh²,

Koh³

et al. 2022

J. Phys.: Conf. Ser.

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Fault diagnosis is increasingly important given the worldwide demand on wind energy as one of the promising renewable energy sources. This systematic review aimed to summarize the fault diagnosis using Extreme Learning Machine (ELM) on wind energy. Firstly, two databases (i.e. Engineering Village (EV) and IEEE Explore were searched to identify relevant articles, using three important keywords, including Extreme Learning Machine/ELM, fault and wind. Of the 14 included studies, only eight studies mentioned the use of sensor to collect vibration signals as the fault data. Sensors were commonly installed at four places (gearbox, generator, bearing, or rotor) in the included studies. Only nine studies used either single or fusion feature extractions for the fault data. Two types of ELM (i.e. single/multi-layered or hybrid-ELM) were identified to diagnose fault. In general, studies showed the superiority of the application of ELM in producing accuracy results in fault diagnosis of WT, compared to other algorithms. Future studies should incorporate the use of real-world data, and improve on the reporting on the methodological components of the study, to better inform on the usefulness of ELM for fault diagnosis in real-world wind energy settings.

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