Renewable Energy Systems With Wind Power

Blaabjerg, Frede; Ma, Ke

doi:10.1002/9781119515661.ch6

Cited by 12 publications

(13 citation statements)

References 44 publications

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“…Compared to the WT systems in today's markets, where a WT power rating is typically at 2-3 MW, the 50-kW power level is relatively low. Now, the rating of a single WT system is approaching 15 MW [19], [60], as indicated in Fig. 9.…”

Section: B Conversion Architecture For Large-scale Wind Systemsmentioning

confidence: 99%

“…Many solutions have been proposed in the literature [19], [62] to integrate large-scale wind energy into the grid. Among those, most wind farms are currently connected to the grid via medium-voltage ac (MVac) technology, as shown in Fig.…”

Section: Fig 9 Development Of Wt Technologies Where "Green Area" Indi...mentioning

confidence: 99%

“…In this context, the large-scale adoption of power electronics for REN resources, being inverterbased, is making the utility grid more complicated, even to a point where the stability might be challenged [14], [15], [16], [17], [18]. On the other hand, with the advancements of power semiconductor devices (e.g., highvoltage blocking capability and high efficiency), the power converters, as the core of energy conversion from REN resources to electricity, provide more controllability and flexibility; however, the design and control should still be further advanced [19], [20], [21], [22], [23], [24] toward systems that are more intelligent, lower cost, and more efficient. At the same time, the design and control of the employed power converters should be more open and/or standardized to address the challenge of modeling "black boxes" from different manufacturers that are often proprietary to the companies.…”

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confidence: 99%

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Power Electronics Technology for Large-Scale Renewable Energy Generation

et al. 2023

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Power electronics is the enabling technology for the grid integration of large-scale renewable energy generation, which provides high controllability and flexibility to energy generation, conversion, transmission, and utilization. However, power electronics also brings several challenges to conventional power grids, e.g., reducing the system inertia. Advanced control strategies have been developed to enhance the energy conversion process. In this article, the grid-integration structure and control for renewable energy are discussed with a focus on large-scale wind, solar photovoltaic, and energy storage systems. Future research and development trends for these technologies are also presented.

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Section: B Conversion Architecture For Large-scale Wind Systemsmentioning

confidence: 99%

Section: Fig 9 Development Of Wt Technologies Where "Green Area" Indi...mentioning

confidence: 99%

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confidence: 99%

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Power Electronics Technology for Large-Scale Renewable Energy Generation

et al. 2023

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“…The best-known types of wind farm infrastructure are offshore wind farms and onshore wind farms [68]. Onshore wind power plants are characterized by relatively low investment and maintenance costs and high predictability of wind parameters [69]. Despite this, currently, offshore wind farms are becoming increasingly popular [70].…”

Section: Renewable Energy Sourcesmentioning

confidence: 99%

Identification of Relevant Criteria Set in the MCDA Process—Wind Farm Location Case Study

2020

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The paper undertakes the problem of proper structuring of multi-criteria decision support models. To achieve that, a methodological framework is proposed. The authors’ framework is the basis for the relevance analysis of individual criteria in any considered decision model. The formal foundations of the authors’ approach provide a reference set of Multi-Criteria Decision Analysis (MCDA) methods (TOPSIS, VIKOR, COMET) along with their similarity coefficients (Spearman correlation coefficients and WS coefficient). In the empirical research, a practical MCDA-based wind farm location problem was studied. Reference rankings of the decision variants were obtained, followed by a set of rankings in which particular criteria were excluded. This was the basis for testing the similarity of the obtained solutions sets, as well as for recommendations in terms of both indicating the high significance and the possible elimination of individual criteria in the original model. When carrying out the analyzes, both the positions in the final rankings, as well as the corresponding values of utility functions of the decision variants were studied. As a result of the detailed analysis of the obtained results, recommendations were presented in the field of reference criteria set for the considered decision problem, thus demonstrating the practical usefulness of the authors’ proposed approach. It should be pointed out that the presented study of criteria relevance is an important factor for objectification of the multi-criteria decision support processes.

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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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Renewable Energy Systems With Wind Power

Cited by 12 publications

References 44 publications

Power Electronics Technology for Large-Scale Renewable Energy Generation

Power Electronics Technology for Large-Scale Renewable Energy Generation

Identification of Relevant Criteria Set in the MCDA Process—Wind Farm Location Case Study

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

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