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Journal articles on the topic 'Wind power'

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Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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1

Carroll, Paula, Lucy Cradden, and Mícheál Ó hÉigeartaigh. "High Resolution Wind Power and Wind Drought Models." International Journal of Thermal and Environmental Engineering 16, no. 1 (August 9, 2018): 27–36. http://dx.doi.org/10.5383/ijtee.16.01.004.

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2

Nah, Do-Baek, Hyo-Soon Shin, and Duck-Joo Nah. "Offshore Wind Power, Review." Journal of Energy Engineering 20, no. 2 (June 30, 2011): 143–53. http://dx.doi.org/10.5855/energy.2011.20.2.143.

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3

Obukhov, S. G. "DYNAMIC WIND SPEED MODEL FOR SOLVING WIND POWER PROBLEMS." Eurasian Physical Technical Journal 17, no. 1 (June 2020): 77–84. http://dx.doi.org/10.31489/2020no1/77-84.

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4

Prajapati, Urvashi, Deepika Chauhan, and Md Asif Iqbal. "Hybrid Solar Wind Power Generation." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1533–37. http://dx.doi.org/10.31142/ijtsrd11359.

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5

Trejos–Grisales, Luz, Cristian Guarnizo–Lemus, and Sergio Serna. "Overall Description of Wind Power." Ingeniería y Ciencia 10, no. 19 (January 2014): 99–126. http://dx.doi.org/10.17230/ingciencia.10.19.5.

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This paper presents a general overview of the main characteristics of the wind power systems, also considerations about the simulation models andthe most used Maximum Power Point Tracker (MPPT) techniques are made. Some simulation results are shown and conclusions about the workare given.

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6

Liu, Tianshu, RS Vewen Ramasamy, Ryne Radermacher, William Liou, and David Moussa Salazar. "Oscillating-wing unit for power generation." Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 233, no. 4 (September 19, 2018): 510–29. http://dx.doi.org/10.1177/0957650918790116.

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This paper describes an exploratory study of a nonconventional wind power converter with a pair of oscillating wings, which is called an oscillating-wing unit. The working principles of the oscillating-wing unit are described, including the aerodynamic models, kinematical, and dynamical models. The performance of the oscillating-wing unit is evaluated through computational simulations and the power scaling in comparison with conventional horizontal-axis wind turbines. Then, a model oscillating-wing unit is designed, built, and tested in a wind tunnel to examine the feasibility of the oscillating-wing unit in extraction of the wind energy in comparison with the theoretical analysis. The theoretical analysis and experimental data indicate that the oscillating-wing unit has the power efficiency comparable to the conventional horizontal axis wind turbine and it can operate at low wind speeds.

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7

Green, K. H. "Wind power." IEE Review 39, no. 1 (1993): 29. http://dx.doi.org/10.1049/ir:19930011.

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8

ARAKAWA, Chuichi. "Wind Power." Journal of the Society of Mechanical Engineers 109, no. 1052 (2006): 549–52. http://dx.doi.org/10.1299/jsmemag.109.1052_549.

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9

Gipe, Paul. "“Wind Power”." Wind Engineering 28, no. 5 (September 2004): 629–31. http://dx.doi.org/10.1260/0309524043028145.

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10

Carlman, Inga. "Wind power in Denmark! Wind power in Sweden?" Journal of Wind Engineering and Industrial Aerodynamics 27, no. 1-3 (January 1988): 337–45. http://dx.doi.org/10.1016/0167-6105(88)90048-7.

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11

Kennedy, J., B. Fox, and J. Morrow. "Working with wind - wind power." Engineering & Technology 3, no. 3 (February 23, 2008): 52–55. http://dx.doi.org/10.1049/et:20080313.

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12

Solovev, Bogdan, and Giorgi Gamisonia. "WIND POWER PREDICTION METHODS FOR SHELF WIND POWER PLANTS." Electrical and data processing facilities and systems 18, no. 3-4 (2022): 108–20. http://dx.doi.org/10.17122/1999-5458-2022-18-3-4-108-120.

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Relevance Wind energy forecasting is an opportunity to evaluate the production possibilities of a wind farm in the short term. Production often refers to the available capacity of the wind farm in question. For example, to date, the installed wind power in Russia has reached 20 GW. Direct transmission operators use existing tools to forecast wind production up to 48 hours. Forecasting tools help optimize power system management. This article discusses the abundance of relevant forecasting methods in the field of wind energy, evaluates their effectiveness and value for the most effective control of wind energy. Particular attention is paid to the ongoing development of wind energy forecasting models to meet the specifics of shelf. Aim of research Conduct a comparative analysis of existing forecasting methods in the field of wind energy under general given conditions, choose the best method for a particular case. Research methods To solve the problem, the authors conducted a comparative analysis of the popular, currently existing methods for forecasting wind farms, comparing their applicability with the specification of the area of use. Results In the course of the study, modern wind energy forecasting tools were analyzed, a comparative analysis was carried out, and conclusions were drawn about the applicability of each of the methods. Keywords: wind energy, short-term forecasting, shelf, optimization, efficiency, model, tool, control, mathematical model, forecast error level

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13

Rehman, Shafiqur, Kashif Irshad, Nasiru I. Ibrahim, Ali AlShaikhi, and Mohamed A. Mohandes. "Offshore Wind Power Resource Assessment in the Gulf of North Suez." Sustainability 15, no. 21 (October 25, 2023): 15257. http://dx.doi.org/10.3390/su152115257.

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Growing population, industrialization, and power requirements are adversely affecting the environment through increased greenhouse gases resulting from fossil fuel burning. Global greenhouse gas mitigation targets have led nations to promote clean and self-renewable sources of energy to address this environmental issue. Offshore wind power resources are relatively more attractive due to high winds, less turbulence, minimal visualization effects, and no interaction of infrastructure. The present study aims at conducting an offshore wind power resource assessment (OWPRA) at some locations in the Gulf of North Suez. For this purpose, the long-term hourly mean wind speed (WS) and wind direction above mean sea level (AMSL), as well as temperature and pressure data near the surface, are used. The data is obtained from ERA5 (fifth generation global climate reanalysis) at six (L1–L6) chosen offshore locations. The data covers a period of 43 years, between 1979 and 2021. The WS and direction are provided at 100 m AMSL, while temperature and pressure are available near water-surface level. At the L1 to L6 locations, the log-term mean WS and wind power density (WPD) values are found to be 7.55 m/s and 370 W/m2, 6.37 m/s and 225 W/m2, 6.91 m/s and 281 W/m2, 5.48 m/s and 142 W/m2, 4.30 m/s and 77 W/m2, and 5.03 and 115 W/m2 and at 100 m AMSL, respectively. The higher magnitudes of monthly and annual windy site identifier indices (MWSI and AWSI) of 18.68 and 57.41 and 12.70 and 42.94 at the L1 and L3 sites, and generally lower values of wind variability indices, are indicative of a favorable winds source, which is also supported by higher magnitudes of mean WS, WPD, annual energy yields, plant capacity factors, and wind duration at these sites. The cost of energy for the worst and the best cases are estimated as 10.120 USD/kWh and 1.274 USD/kWh at the L5 and L1 sites, corresponding to wind turbines WT1 and WT4. Based on this analysis, sites L1, L3, and L2 are recommended for wind farm development in order of preference. The wind variability and windy site identifier indices introduced will help decision-makers in targeting potential windy sites with more confidence.

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14

Pereyra-Castro, Karla, Ernesto Caetano, Oscar Martínez-Alvarado, and Ana L. Quintanilla-Montoya. "Wind and Wind Power Ramp Variability over Northern Mexico." Atmosphere 11, no. 12 (November 27, 2020): 1281. http://dx.doi.org/10.3390/atmos11121281.

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The seasonal and diurnal variability of the wind resource in Northern Mexico is examined. Fourteen weather stations were grouped according to the terrain morphology and weather systems that affect the region to evaluate the impact on wind ramps and high wind persistent events. Four areas driven by weather systems seasonality are identified. Wind power ramps and persistent generation events are produced by cold fronts in winter, while mesoscale convective systems and local circulations are dominant in summer. Moreover, the 2013 wind forecast of the Rapid Refresh Model (RAP) and the North American Mesoscale Forecast System (NAM) forecast systems were also assessed. In general, both systems have less ability to predict mesoscale events and local circulations over complex topography, underestimating strong winds and overestimating weak winds. Wind forecast variations in the mesoscale range are smoother than observations due to the effects of spatial and temporal averaging, producing fewer wind power ramps and longer lasting generation events. The study carried out shows the importance of evaluating operational models in terms of wind variability, wind power ramps and persistence events to improve the regional wind forecast. The characteristics of weather systems and topography of Mexico requires model refinements for proper management of the wind resource.

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15

Bao-Wei Zhang, Bao-Wei Zhang, Hong-Bo Cui Bao-Wei Zhang, and Jiu-Xiang Song Hong-Bo Cui. "Wind Power Prediction Based on Difference Method." 電腦學刊 33, no. 4 (August 2022): 195–204. http://dx.doi.org/10.53106/199115992022083304016.

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<p> The renewable wind power sources are difficult to be predicted in view of the fluctuating factors such as wind bearing, pressure, wind speed, and humidity of the surrounding atmosphere. An attempt is made in this paper to propose a difference method to build a neural network and a long short term memory (LSTM) model for wind power prediction. First, the correlation of each data is analyzed and then per-forming difference processing on the original data to solve the problem that the original data cannot be analyzed by probability distribution. The prediction is made by building the neural network and LSTM and feeding the original data and the difference-processed data into the neural network model respective-ly. Finally, the data are added for validation, and the raw data used include wind power data in Belgium from November 1, 2019 to November 30, 2019.The experimental results show that the LSTM prediction accuracy is improved by 178.67%, and is effective in predicting long-term wind power data with 216.06% accuracy improvement, the neural network prediction accuracy is improved by 154.07%, and the short-term wind power prediction accuracy is improved by 228%.</p> <p> </p>

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16

Lewis, D. "Putting the wind up [wind power]." Engineering & Technology 4, no. 3 (February 14, 2009): 52–55. http://dx.doi.org/10.1049/et.2009.0312.

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17

DeMeo, E. A., W. Grant, M. R. Milligan, and M. J. Schuerger. "Wind plant integration [wind power plants." IEEE Power and Energy Magazine 3, no. 6 (November 2005): 38–46. http://dx.doi.org/10.1109/mpae.2005.1524619.

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18

Mohammadha Hussaini, M., and R. Anita. "Power Quality Analysis in Wind Power Generation Using Sliding Mode Control." International Journal of Engineering and Technology 2, no. 5 (2010): 481–85. http://dx.doi.org/10.7763/ijet.2010.v2.168.

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19

Karimov, S. K. O. "Germany. Wind power." Trends in the development of science and education 59, no. 2 (March 31, 2020): 40–43. http://dx.doi.org/10.18411/lj-03-2020-28.

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20

Mozina, Charles. "Wind-Power Generation." IEEE Industry Applications Magazine 17, no. 3 (May 2011): 37–43. http://dx.doi.org/10.1109/mias.2010.939636.

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21

Marris, Emma. "Global wind power." Nature 454, no. 7202 (July 2008): 264. http://dx.doi.org/10.1038/454264b.

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22

Jagadeesh, A. "Indian wind power." Refocus 2, no. 4 (May 2001): 16–18. http://dx.doi.org/10.1016/s1471-0846(01)80043-3.

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23

Weisbrich, Alfred L. "Alternative wind power." Refocus 3, no. 2 (March 2002): 26–29. http://dx.doi.org/10.1016/s1471-0846(02)80024-5.

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24

Kennedy, Barry W. "Integrating wind power." Refocus 5, no. 1 (January 2004): 36–37. http://dx.doi.org/10.1016/s1471-0846(04)00075-7.

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25

Hammell, Darren. "Wind power electronics." Refocus 5, no. 3 (May 2004): 36–38. http://dx.doi.org/10.1016/s1471-0846(04)00142-8.

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26

Coleman, Matt, and Steve Provol. "Wind power economics." Refocus 6, no. 4 (July 2005): 22–24. http://dx.doi.org/10.1016/s1471-0846(05)70426-1.

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27

Syngellakis, Katerina, Steve Carroll, and Peter Robinson. "Small wind power." Refocus 7, no. 2 (March 2006): 40–45. http://dx.doi.org/10.1016/s1471-0846(06)70546-7.

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28

Cawley, Alec. "Wind-power feedback." New Scientist 198, no. 2658 (May 2008): 23. http://dx.doi.org/10.1016/s0262-4079(08)61352-4.

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29

Marks, Paul. "Supercool wind power." New Scientist 217, no. 2900 (January 2013): 19. http://dx.doi.org/10.1016/s0262-4079(13)60154-2.

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30

Dunlop, John. "U.K. Wind Power." Journal of Alternative Investments 7, no. 2 (September 30, 2004): 85–92. http://dx.doi.org/10.3905/jai.2004.439655.

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31

Care, C. M. "Wind power progress." Physics in Technology 17, no. 4 (July 1986): 190–93. http://dx.doi.org/10.1088/0305-4624/17/4/408.

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32

ISHIHARA, Takeshi. "Offshore Wind Power." Journal of the Society of Mechanical Engineers 114, no. 1109 (2011): 262–64. http://dx.doi.org/10.1299/jsmemag.114.1109_262.

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33

Chen, Q., and K. A. Folly. "Wind Power Forecasting." IFAC-PapersOnLine 51, no. 28 (2018): 414–19. http://dx.doi.org/10.1016/j.ifacol.2018.11.738.

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34

Trubaev, K. P. "Study of Power Dependence of Wind Power from Wind Speed." Journal of Physics: Conference Series 1066 (August 2018): 012026. http://dx.doi.org/10.1088/1742-6596/1066/1/012026.

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35

Huang, Yue Hua, Huan Huan Li, and Guang Xu Li. "Maximum Wind Power Tracking Strategy of Wind Power Generation System." Applied Mechanics and Materials 313-314 (March 2013): 813–16. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.813.

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Aiming at maximum wind power tracking control problem of wind power generation system below the rated wind speed, this paper presents an improved MPPT control strategy by using turbulent part of the wind speed as a search signal to find the maximum power point. By using the Matlab/Simulink simulation of the wind power generation system below the rated wind speed, this paper proves the effectiveness of this control strategy. The simulation results show that improved MPPT control strategy can well control the wind turbine speed to track the wind speed changes to maintain optimum tip speed ratio and the maximum power coefficient.

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36

Ren, Bixing, Yongyong Jia, Qiang Li, Dajiang Wang, Weijia Tang, and Sen Zhang. "Robust Wind Power Ramp Control Strategy Considering Wind Power Uncertainty." Electronics 13, no. 1 (January 3, 2024): 211. http://dx.doi.org/10.3390/electronics13010211.

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Recent climate change has worsened the risk of extreme weather events, among which extreme offshore wind storms threaten secure operation by inducing offshore wind power ramps. Offshore wind power ramps cause the instantaneous power fluctuation of interconnected onshore grids and may lead to unexpected load shedding or generator tripping. In this paper, considering offshore wind power uncertainties, we propose a novel robust coordinated offshore wind power ramp control strategy by dispatching thermal units, energy storage systems, and hydrogen storage systems cooperatively. First, the impact of extreme wind storms on an offshore wind farm output power ramp is analyzed, and the general framework of offshore wind power ramp control is presented based on the two-stage robust optimization considering the dual uncertainties of load demand and wind power. Second, a coordinated wind power ramp control model is established considering the operational characteristics of different ramp control sources such as thermal units, energy storage systems, and offshore wind farms. Third, a robust ramp control strategy is developed using the column-and-constraint generation (CC&G) algorithm. Simulation results show the effectiveness of the proposed ramp control strategy.

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37

Cai, Zelin, Tao Feng, Jun Guo, Bo Hu, and Lei Wang. "Wind power short-term prediction over mountain area using a high-resolution WRF model." E3S Web of Conferences 260 (2021): 02012. http://dx.doi.org/10.1051/e3sconf/202126002012.

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Accurate wind power prediction are crucial for power-grid integration and load balancing, as well as the safe and stable operation of the power grid. In this study, the relationship between the wind speed and wind power over mountain area is firstly investigated using the observations in Hunan Baiguoshan Mountain, and the fitting equation is proposed to predict the wind power with wind speed. Using the simulation of the WRF model with a 3-kilometer horizontal resolution, its prediction performance for short-term wind power is further analyzed. The results show that a sixth power relationship exists between wind speeds and wind powers over the mountain area. Also, when the wind speed reaches up to about 9.5 m/s (half of the cut-out wind speeds), the wind power is almost up to its rated power (2200 KW). The evolution characteristics of the wind powers predicted by the WRF model resemble that in observations, but the predicted wind powers are larger than that as observations in most time, which results from the overestimated predicted wind speeds like that in observations.

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38

THOMAS, SIT. "Wind analysis for wind power: Part III." MAUSAM 36, no. 1 (April 5, 2022): 79–82. http://dx.doi.org/10.54302/mausam.v36i1.1600.

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The paper contains frequency distribution of hourly values of winds of Jamnagar in north Saurashtra in the four ranges, viz., light, moderate, strong and very strong with the objective of utilization of energy from winds for the wind mills. Monthwise frequency distribution is also presented. A comparative study has, also been made with the results of similar analyses made earlier in respect of Ahmedabad in the north Gujarat region and Baroda in the south Gujarat region with the present analysis for Jamnagar in north Saurashtra.

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39

Wang, Xiaochun, Tong Lee, and Carl Mears. "Evaluation of Blended Wind Products and Their Implications for Offshore Wind Power Estimation." Remote Sensing 15, no. 10 (May 18, 2023): 2620. http://dx.doi.org/10.3390/rs15102620.

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The Cross-Calibrated Multi-Platform (CCMP) wind analysis is a satellite-based blended wind product produced using a two-dimensional variational method. The current version available publicly is Version 2 (CCMP2.0), which includes buoy winds in addition to satellite winds. Version 3 of the product (CCMP3.0) is being produced with several improvements in analysis algorithms, without including buoy winds. Here, we compare CCMP3.0 with a special version of CCMP2.0 that did not include buoy winds, so both versions are independent of buoy measurements. We evaluate them using wind data from buoys around the coasts of the United States and discuss the implications for the wind power industry and offshore wind farms. CCMP2.0 uses ERA-Interim 10 m winds as the background to fill observational gaps. CCMP3.0 uses ERA5 10 m neutral winds as the background. Because ERA5 winds are biased towards lower values at higher wind conditions, CCMP3.0 corrected this bias by matching ERA5 wind speeds with satellite scatterometer wind speeds using a histogram matching method. Our evaluation indicates that CCMP3.0 has better agreement with the independent buoy winds, primarily for higher winds (>10 m/s). This is reflected by the higher correlation and lower root-mean-squared differences of CCMP3.0 versus buoy winds, especially for higher wind conditions. For the U.S. coastal region (within 200 km), the mean wind speed of CCMP3.0 is enhanced by 1–2%, and the wind speed standard deviation is enhanced by around 3–5%. These changes in wind speed and its standard deviation from CCMP2.0 to CCMP3.0 cause an 8–12% increase in wind power density. The wind power density along the U.S. coastal region is also correlated with various climate indices depending on locations, providing a useful approach for predicting wind power on subseasonal to interannual timescales.

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40

OKUBO, Hiroshi, Ryo HATAKEYAMA, Hidemi ONODERA, Tsuyoshi SATO, Hironori FUJII, Yusuke MARUYAMA, and Makoto IWAHARA. "Airborne Wind Power Generation Using a Straight Wing Vertical Axis Wind Turbine." Proceedings of Conference of Kanto Branch 2019.25 (2019): 18E16. http://dx.doi.org/10.1299/jsmekanto.2019.25.18e16.

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41

Wan, Yih-huei, Michael Milligan, and Brian Parsons. "Output Power Correlation Between Adjacent Wind Power Plants*." Journal of Solar Energy Engineering 125, no. 4 (November 1, 2003): 551–55. http://dx.doi.org/10.1115/1.1626127.

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The National Renewable Energy Laboratory (NREL) started a project in 2000 to record long-term, high-frequency (1-Hz) wind power data from large commercial wind power plants in the Midwestern United States. Outputs from about 330 MW of installed wind generating capacity from wind power plants in Lake Benton, MN, and Storm Lake, Iowa, are being recorded. Analysis of the collected data shows that although very short-term wind power fluctuations are stochastic, the persistent nature of wind and the large number of turbines in a wind power plant tend to limit the magnitude of fluctuations and rate of change in wind power production. Analyses of power data confirms that spatial separation of turbines greatly reduces variations in their combined wind power output when compared to the output of a single wind power plant. Data show that high-frequency variations of wind power from two wind power plants 200 km apart are independent of each other, but low-frequency power changes can be highly correlated. This fact suggests that time-synchronized power data and meteorological data can aid in the development of statistical models for wind power forecasting.

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42

Leonowicz, Z. "Power quality in wind power systems." Renewable Energy and Power Quality Journal 1, no. 07 (April 2009): 234–38. http://dx.doi.org/10.24084/repqj07.303.

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43

Chen, Zhe. "Wind power in modern power systems." Journal of Modern Power Systems and Clean Energy 1, no. 1 (June 2013): 2–13. http://dx.doi.org/10.1007/s40565-013-0012-4.

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44

Ko, Seung-Youn, Ho-Chan Kim, Jong-Chul Huh, and Min-Jae Kang. "Wind Estimation Power Control using Wind Turbine Power and Rotor speed." Journal of the Korea Academia-Industrial cooperation Society 17, no. 4 (April 30, 2016): 92–99. http://dx.doi.org/10.5762/kais.2016.17.4.92.

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45

Wang, Yun, Qinghua Hu, Dipti Srinivasan, and Zheng Wang. "Wind Power Curve Modeling and Wind Power Forecasting With Inconsistent Data." IEEE Transactions on Sustainable Energy 10, no. 1 (January 2019): 16–25. http://dx.doi.org/10.1109/tste.2018.2820198.

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46

Ren, Guorui, Jie Wan, Jinfu Liu, Daren Yu, and Lennart Söder. "Analysis of wind power intermittency based on historical wind power data." Energy 150 (May 2018): 482–92. http://dx.doi.org/10.1016/j.energy.2018.02.142.

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47

Ding, Jiaman, Zhixin Chen, and Yi Du. "Probability box theory-based uncertain power flow calculation for power system with wind power." International Journal of Emerging Electric Power Systems 22, no. 2 (February 19, 2021): 243–53. http://dx.doi.org/10.1515/ijeeps-2020-0227.

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Abstract The uncertainty of wind speed may lead to the deviation and change of wind power output, which influences the stability of wind farm. Therefore, in this paper, a probability box (p-box) based uncertain power flow model for wind power is proposed, which initially introduces p-box to power flow calculation. A probabilistic interval power flow model with both probability and interval is established. Firstly, the drift interval of wind speed is obtained and its p-box model is established by analyzing the distribution of wind speed. Secondly, the wind power output p-box is derived from the wind speed p-box based on the relationship between wind power output and wind speed, then the p-box of wind power output is discretized and introduced into the power flow equation to obtain the power flow p-box model. Finally, Newton–Raphson method is used to solve the power flow p-box model. Experiments on data collected from a wind farm (running standard IEEE30-bus test system) in Inner Mongolia demonstrate that our method is more effective and accurate than the traditional Monte Carlo simulation (MCS) and classical interval power flow (IPF) method.

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48

Ouchi, Kazuyuki. "“Wind Challenger Project” Utilizing Ocean Wind Power." Marine Engineering 47, no. 4 (2012): 566–71. http://dx.doi.org/10.5988/jime.47.566.

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49

Song, Hai Hui, Jian Jun Wang, Zhi Hua Hu, and Jin Zhou. "Research on Low-Wind-Speed Wind Power." Applied Mechanics and Materials 448-453 (October 2013): 1811–14. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.1811.

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For high-wind-speed wind power development and problems, propose development and application of low-wind-speed wind power (LWSP). Analysis of the characteristics of LWSP , advantages and necessity of development and application of it. Research the key technologies of LWSP development. It ultimately lay the foundation for research, development and application of LWSP technologies.

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50

VISHNOI, B. N., and VIRENDRA SINGH. "WIND ANALYSIS FOR WIND POWER AT JAISALMER." MAUSAM 56, no. 4 (January 20, 2022): 904–7. http://dx.doi.org/10.54302/mausam.v56i4.1087.

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