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Relevant bibliographies by topics / Wind power generation

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Academic literature on the topic 'Wind power generation'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 June 2025

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Journal articles on the topic "Wind power generation"

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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 (2018): 1533–37. http://dx.doi.org/10.31142/ijtsrd11359.

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

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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 (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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M, Vaghela P., Thorat P. D, and Lakudzode K. B. Prof Udamle S. R. "Train Mounting T-Box for Wind Power Generation." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (2019): 898–901. http://dx.doi.org/10.31142/ijtsrd23933.

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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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Khan, M. F., and M. R. Khan. "Wind Power Generation in India: Evolution, Trends and Prospects." International Journal of Renewable Energy Development 2, no. 3 (2013): 175–86. http://dx.doi.org/10.14710/ijred.2.3.175-186.

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In the present context of shrinking conventional resources coupled with environmental perils, the wind power offers an attractive alternative. Wind power generation in India started way back in early 1980s with the installation of experimental wind turbines in western and southern states of Gujarat and Tamil Nadu. For first two decades of its existence until about 2000 the progress was slow but steady. In last one decade Indian wind electricity sector has grown at very rapid pace which has promoted the country to the fifth position as largest wind electric power generator and the third largest market in the world. The galvanization of wind sector has been achieved through some aggressive policy mechanisms and persistent support by government organizations such as MNRE and C-WET. This paper articulates the journey of Indian wind program right since its inception to the present trends and developments as well as the future prospects. Keywords: mnre, c-wet, renewable energy, wind power, wind turbines.

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Kyozuka, Yusaku. "Offshore Wind Power Generation." Marine Engineering 47, no. 4 (2012): 549–53. http://dx.doi.org/10.5988/jime.47.549.

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Sguarezi Filho, Joãozinho, André Luiz de Lacerda Ferreira Murari, Carlos Eduardo Capovilla, José Alberto Torrico Altuna, and Rogério Vani Jacomini. "A State Feedback Dfig Power Control For Wind Generation." Eletrônica de Potência 20, no. 2 (2015): 151–59. http://dx.doi.org/10.18618/rep.2015.2.151159.

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Kim, Sunoh, and Jin Hur. "Probabilistic Approaches to the Security Analysis of Smart Grid with High Wind Penetration: The Case of Jeju Island’s Power Grids." Energies 13, no. 21 (2020): 5785. http://dx.doi.org/10.3390/en13215785.

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As the importance of renewable generating resources has grown around the world, South Korea is also trying to expand the proportion of renewable generating resources in the power generation sector. Among the various renewable energy sources, wind generating resources are emerging as a key alternative to conventional power generations in the electricity sector in Korea accounted for 17.7 GW of total capacity by 2030. As wind generating resources are gradually replacing traditional generating resources, the system security and reliability are negatively affected because of the variability, due to intermittent outputs. Therefore, existing power grids will need to be correctly re-measured to cover the large scale of renewable energy, including wind generation. To expand the grid, we must understand the characteristics of renewable energy and the impact of its adoption in the grid. In this paper, we analyze various characteristics of wind power generation, and then we propose a probabilistic power output modeling method to consider the uncertainty of wind power generation. For the probabilistic approach, Monte-Carlo simulation is used in the modeling method. The modeled wind power outputs can help planning for the reinforcement and expansion of power systems to expand the capacity for large-scale renewable energy in the future. To verify the proposed method, some case studies were performed using empirical data, and probabilistic power flow calculation was performed by integrating large-scale wind power generation to the Jeju Island power system. The probabilistic method proposed in this paper can efficiently plan power system expansion and play a key strategy of evaluating the security of the power system through the results of stochastic power flow calculation.

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Ghodake, Rahul, Hrutik Kamble, Ishan Sutar, Tushar Mohite, Anurag Sutar, and Latif Sohel. "Windmill Power Generation System." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 1442–44. http://dx.doi.org/10.22214/ijraset.2023.49671.

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Abstract: Wind turbines are the most ancient known means of extracting energy from natural sources (wind in this case). It is not possible to create high consistent power from a wind turbine due to changeable weather and wind speed, but a small-scale wind turbine can be used to power tiny appliances at home and in moving vehicles. This research investigates the design features of an innovative small-scale wind turbine intended to supply power. The paper covers design, improvement, power management, and production procedures.

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Dissertations / Theses on the topic "Wind power generation"

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SILVA, ILITCH VITALI GOMES DA. "THE WIND FORECAST FOR WIND POWER GENERATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16824@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>A energia eólica é uma das alternativas mais promissoras para geração de energia elétrica, pois assegura a diversidade e segurança no fornecimento de energia e atende à necessidade premente de reduzir os níveis de emissão de gases poluentes. Na operação de sistemas elétricos com forte presença de geração eólica é fundamental prever com pelo menos um dia de antecedência os valores futuros (pelo menos horários) da veloci-dade do vento, pois assim pode-se avaliar a disponibilidade de energia para o próximo dia, uma informação útil no despacho das unidades geradoras e no controle do sistema elétrico. A proposta dessa dissertação objetiva especificamente desenvolver modelos de previsão de curto prazo da velocidade do vento, baseado em técnicas de inteligência artificial, modelo da rede neural artificial e neuro-fuzzy adaptativa (ANFIS) e um mode-lo Estatístico composto por um modelo de regressão harmônica e Box-Jenkins. Para aplicação da metodologia considerou-se o município de São João do Cariri (Estado de Paraíba), onde está localizada uma das estações de referência do projeto SONDA (Sis-tema Nacional de Dados Ambientais para o setor de energia). O desempenho dos mode-los rede neural, neuro-fuzzy (ANFIS) e modelo Estatístico são comparados nas previ-sões de 6 horas, 12 horas, 18 h e 24horas a frente. Os resultados obtidos mostram o me-lhor desempenho da modelagem ANFIS e encorajam novos estudos no tema.<br>Wind power is one of the most promising options for power generation. It ensures the diversity and security of energy supply and meets the pressing need to reduce the levels of emission of polluting gases. In the operation of electrical systems with a strong presence of wind generation, it is essential to provide at least one day in advance the future values (at least hourly) of wind speed, so that we can assess the availability of energy for the next day, a useful information in the order of the generating units and electrical control system. The purpose of this dissertation aims to develop models spe-cifically to develop models to forecast short-term wind speed, based on artificial intelligence techniques, artificial neural network model and adaptive neuro-fuzzy Systems (ANFIS) and a statistical model composed of a harmonic regression model and Box-Jenkins. For application of the methodology, the city of São João do Cariri (State of Paraíba), where a reference station of SONDA project (National Environmental Data for the energy sector) is located, was considered.To apply the methodology was consi-dered the city of the ray tracing model (State of Paraíba), which is located a station ref-erence design (National Environmental Data for the energy sector). The performance of artificial neural network model and adaptive neuro-fuzzy Systems (ANFIS) and a statis-tical model are compared mixed forecasts of 6 hours, 12 hours, 18hours and 24 hours ahead. The results show the best performance of the ANFIS model and encourage fur-ther studies on the subject.

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Ndzukuma, Sibusiso. "Statistical tools for wind energy generation." Thesis, Nelson Mandela Metropolitan University, 2012. http://hdl.handle.net/10948/d1020627.

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In this study we conduct wind resource assessment to evaluate the annual energy production of a wind turbine. To estimate energy production of a wind turbine over a period of time, the power characteristics of the wind turbine are integrated with the probabilities of the wind speed expected at a chosen site. The first data set was obtained from a wind farm in Denmark. We propose several probability density functions to model the distribution of the wind speed. We use techniques from nonlinear regression analysis to model the power curve of a wind turbine. The best fit distribution model is assessed by performing numeric goodness–of–fit measures and graphical analyses. Johnson’s bounded (SB) distribution provides the best fit model with the smallest Kolmogorov–Smirnov (K-S) test statistic . 15. The four parameter logistic nonlinear regression (4PL) model is determined to provide the best fit to the power curve data, according to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The estimated annual energy yield is compared to the actual production of the wind turbine. Our models underestimate the actual energy production by a 1 difference. In Chapter Six we conduct data processing, analyses and comparison of wind speed distributions using a data set obtained from a measuring wind mast mounted in Humansdorp, Eastern Cape. The expected annual energy production is estimated by using the certified power curve as provided by the manufacturer of the wind turbine under study. The commonly used Weibull distribution is determined to provide the best fit distribution model to our selected models. The annual energy yield is estimated at 7.33 GWh, with a capacity factor of 41.8 percent.

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Navarrete, Pablo-Romero Javier. "Power Quality for Distributed Wind Power Generation." Thesis, KTH, Elektroteknisk teori och konstruktion, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105221.

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Wind power often is a source of voltage fluctuations and possible voltage issues are raised when considering interconnecting wind turbines to an electric grid. Also, the power electronics introduced in the wind turbines might insert more fluctuations and different PQ problems. Distributed generation seems to be a good option in order to try to mitigate these problems. The first goal of the work is to create a model of a small electric grid, using MATLAB/Simulink. The models aims to simulate various DFIG wind turbines coupled to the grid in different conditions of location and wind. Then, the main objective is to analyze the PQ in the grid with this type of turbine. For this, once the simulations have been done, the results obtained have allowed calculating different indices to study PQ in the model. Afterwards, a comparison of those indices in the different conditions is made.<br>StandUp

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Montenegro, León Alejandro. "Advanced power electronic for wind-power generation buffering." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0010112.

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Li, L. "Maximum power control of permanent magnet synchronous generator based wind power generation systems." Thesis, University of Liverpool, 2016. http://livrepository.liverpool.ac.uk/3006695/.

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Fisher, Samuel Martin. "Improving the reliability of wind power through spatially distributed wind generation." OpenSIUC, 2012. https://opensiuc.lib.siu.edu/theses/897.

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Wind power is a fast-growing, sustainable energy source. However, the problem of wind variability as it relates to wind power reliability is an obstacle to its large-scale deployment. It is possible to improve the reliability of wind power by interconnecting wind generation. In this study, wind power plants within the Midwest ISO were aggregated to examine the effect on reliability. Wind speed data from the North American Regional Reanalysis were used to calculate wind power data. It was found that the reliability of interconnected wind power was improved relative to individual wind power plants in both the short-term and the long-term, and that the most significant improvements were at the highest scales of interconnection. It was also found that the reliability of interconnected wind power is more directly related to the area of the network rather than the number of wind power plants in the network.

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Edwards, Gregory W. "Wind turbine power generation emulation via doubly fed induction generator control." Thesis, Monterey, California : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Dec/09Dec%5FEdwards.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, December 2009.<br>Thesis Advisor(s): Julian, Alexander L. Second Reader: Cristi, Roberto. "December 2009." Description based on title screen as viewed on January 28, 2010. Author(s) subject terms: Double Fed Induction Generator (DFIG), Space Vector Modulation (SVM), wind turbine, Field Programmable Gate Array (FPGA), bi-directional power flow. Includes bibliographical references (p. 75). Also available in print.

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Castro, Manuel. "Generation and transmission adequacy evaluation of power systems with wind generation." Thesis, Imperial College London, 2009. http://hdl.handle.net/10044/1/5267.

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In response to the challenge of proposed reductions to greenhouse gas emissions outlined in international agreements such as the Kyoto Protocol, countries are considering supplying a significant share of their future energy requirements from renewable energy sources. Wind power, both on and offshore, is the principal commercially available and scaleable renewable energy technology. It is expected to remain the dominant technology in the medium-term future by delivering the majority of the required growth in renewable energy. The unique characteristics of wind power generation raise issues for its integration into the existing power systems. This thesis explores three specific issues, namely, wind generation’s limited capacity value, its remoteness from demand centres and the appropriateness of the regulatory framework governing its integration. The first issue was addressed by examining how the presence of flexible generation sources like hydro power affects the capacity value of wind in an assessment of overall system generation capacity. Wind capacity credit is interpreted from a planning perspective, and also as a component of the economic value of wind. The results illustrate that hydro power can compensate the variability of wind generation thereby augmenting its capacity value. The second issue required the development of a transmission planning methodology to evaluate the sufficiency of transmission network capacity to accommodate wind generation and to manage security of supply. The methodology was used to assess, over the long term investment horizon, the requirement for additional transmission network capacity driven by wind generation. The assessment found that wind generation drives less transmission network capacity than conventional generation and that wind and conventional generation should share the same transmission network capacity. Finally, the thesis looked into the establishment of regulatory framework that could recognise the realistic contribution of wind generation characteristics to transmission security and capture this contribution within the network pricing structure. The current 4 transmission security standards were reviewed to evaluate whether they are capable of recognising the different operation characteristics and output of wind generation. Standards for assessing transmission adequacy were found to lead to under-investment in capacity for importing areas and over-investment in exporting areas. Consequently, a set of ‘contribution factors’ capturing the interaction between wind and system characteristics were derived to augment the standards. At the same time, a modification of the present TNUoS charging mechanism in order to discriminate between generation technology types and to devise cost-reflective pricing regimes is proposed. This is particularly important when transmission investment is driven by reliability, as in exporting areas the cost reflective charges for wind were uniformly found to be lower than the charges for conventional generators.

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Cheng, Alan Yung Chen. "Economic modeling of intermittency in wind power generation." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32284.

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Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.<br>Includes bibliographical references (p. 59-61).<br>The electricity sector is a major source of carbon dioxide emissions that contribute to global climate change. Over the past decade wind energy has steadily emerged as a potential source for large-scale, low carbon energy. As wind power generation increases around the world, there is increasing interest in the impacts of adding intermittent power to the electricity grid and the potential costs of compensating for the intermittency. The goal of this thesis research is to assess the costs and potential of wind power as a greenhouse gas abatement option for electricity generation. Qualitative and quantitative analysis methods are used to evaluate the challenges involved in integrating intermittent generation into the electricity sector. A computable generation equilibrium model was developed to explicitly account for the impacts of increasing wind penetration on the capacity value given to wind. The model also accounts for the impacts of wind quality and geographic diversity on electricity generation, and the impacts of learning-by-doing on the total cost of production. We notice that the rising costs associated of intermittency will limit the ability of wind to take a large share of the electricity market. As wind penetration increases, a greater cost is imposed on the wind generator in order to compensate for the intermittency impacts, making the total cost from energy from wind more expensive. Because the model explicitly accounts for the impacts of intermittency, the decision to add wind power to the grid is based on the marginal cost of adding additional intermittent sources to the system in addition to the cost of generating wind energy.<br>(cont.) This model was incorporated into the MIT Emissions Prediction and Policy Analysis model in order to analyze the adoption of wind technology under three policy scenarios. In a business as usual scenario with no wind subsidies or carbon constraints, wind energy generation rises to 0.80 trillion KWh in 2090 and accounts for 9% of the total electricity generation. In a scenario that stabilized greenhouse gases at 550 parts per million, high carbon penalties motivate the entry of 1.16 trillion KWh of wind energy generation in 2055 that accounts for 22% of the total electricity generation. With a production tax credit subsidy for wind generation, wind energy generation increases by average of 12% over the base case scenario during the years the policy was in effect. However, when the subsidy tapers off, wind generation in later periods remains unchanged.<br>by Alan Yung Chen Cheng.<br>S.M.

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Liu, Baicun. "Optimal power flow dispatch of variable wind generation." Thesis, Liu, Baicun (2016) Optimal power flow dispatch of variable wind generation. Honours thesis, Murdoch University, 2016. https://researchrepository.murdoch.edu.au/id/eprint/30812/.

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Wind power is one of the most popular renewable energy resources in the world. However, the variability of wind power leads to a situation in which a wind turbine only system cannot exist. Optimization of wind power flow dispatch is a long term work. And the variable wind generation in optimal power flow dispatching by mathematical model is an initial step in the operational process. It is because that the mathematical module in simulation software is almost no cost, simple and easy to go. The assumption in this thesis is that wind turbine is sited in the same location as the Plymouth.[8] The cost of being sited in a research different location and long distance power transmission will be discussed as future research. The optimization modelling is built using Scilab simulation. Scilab is an engineering simulation software, and Scialb is similar with MATLAB. The characteristic of Scilab is the open source coding. It is means users can coding program by using Scilab languages as their requirement. To built a network, the equation of different cost has been distinguished, which include the thermal only system cost as a test system, and the wind power cost. The variable value of wind information is set as wind turbine scheduled power rather than wind speed. For a given system, the scheduled power canbe calculated from wind speed, however, the wind speed calculation is not animportant partof this paper, and it is only briefly discussed. This study present a method to incorporate, the variable wind generation in optimal power flow dispatching and can find the best power set up by calculating scheduled power of wind turbine and generators power. The fact work of intermittent wind generation in optimal power flow dispatching will be talk with the result analysis. For the result of the simulation, the program can calculate a best power flow dispatch which has the lowest average cost in a given network.

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Books on the topic "Wind power generation"

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Li, Zhang, ed. Electricity generation using wind power. World Scientific, 2010.

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Tong, Wei. Wind power generation and wind turbine design. WIT Press, 2010.

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Rao, K. R. Wind Energy for Power Generation. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-75134-4.

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Valuing wind generation on integrated power systems. William Andrew/Elsevier, 2010.

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Stiebler, M. Wind energy systems for electric power generation. Springer, 2010.

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Amaris, Hortensia, Monica Alonso, and Carlos Alvarez Ortega. Reactive Power Management of Power Networks with Wind Generation. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4667-4.

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Wind energy basics: A guide to distributed wind energy. 2nd ed. Chelsea Green Pub. Company, 2009.

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Monaldo, Frank. Maryland offshore wind climatology with application to wind power generation. Maryland Power Plant Research Program, 2011.

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Kikō, Nihon Bōeki Shinkō. The study on environmental assessment of wind-power generation projects in the United Arab Emirates. Japan External Trade Organization, 2010.

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Olimpo, Anaya-Lara, ed. Wind energy generation: Modelling and control. John Wiley & Sons, 2009.

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Book chapters on the topic "Wind power generation"

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Eicke, Anselm, Laima Eicke, and Manfred Hafner. "Wind Power Generation." In The Palgrave Handbook of International Energy Economics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-86884-0_10.

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AbstractWind power plays a major role in the decarbonization of the power sector. Already now, it supplies increasing shares of the global energy demand. This book chapter provides an overview on the economics of wind energy and highlight global trends in the wind sector. It describes the technical characteristics of onshore and offshore wind energy and explains how these affect the economic competitiveness of the respective technologies. The authors describe how wind power, as an intermittent source of energy, can be integrated into power systems. They also discuss how renewable energy support schemes contribute in fostering the deployment of wind power.

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Shahidehpour, Mohammad, and Mircea Eremia. "Wind Power Generation." In Handbook of Electrical Power System Dynamics. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118516072.ch4.

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Nakanishi, Yosuke, Tetsuo Saito, and Ryuichi Yokoyama. "Wind Power Generation." In Energy Technology Roadmaps of Japan. Springer Japan, 2016. http://dx.doi.org/10.1007/978-4-431-55951-1_19.

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Singh, Mohit, Eduard Muljadi, Vahan Gevorgian, and Surya Santoso. "Wind Power Generation." In Power Electronics for Renewable and Distributed Energy Systems. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5104-3_4.

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Gasch, Robert, and Jochen Twele. "Wind turbines for electricity generation - basics." In Wind Power Plants. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22938-1_11.

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Gasch, Robert, and Jochen Twele. "Concepts of electricity generation by wind turbines." In Wind Power Plants. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22938-1_13.

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Qureshi, Waqar A., and Nirmal-Kumar C. Nair. "Wind Farm Protection." In Large Scale Renewable Power Generation. Springer Singapore, 2014. http://dx.doi.org/10.1007/978-981-4585-30-9_12.

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Rao, K. R. "Wind Energy Economics." In Wind Energy for Power Generation. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-75134-4_2.

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Amaris, Hortensia, Monica Alonso, and Carlos Alvarez Ortega. "Wind Generators." In Reactive Power Management of Power Networks with Wind Generation. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4667-4_3.

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Schmehl, Roland, Michael Noom, and Rolf van der Vlugt. "Traction Power Generation with Tethered Wings." In Airborne Wind Energy. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39965-7_2.

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Conference papers on the topic "Wind power generation"

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Ma, Longpeng, Chen Wu, Kaihui Nan, et al. "Wind Power Scenario Generation Considering Wind Power Variations." In 2022 IEEE 5th International Electrical and Energy Conference (CIEEC). IEEE, 2022. http://dx.doi.org/10.1109/cieec54735.2022.9846022.

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Moore, A. "Challenges of wind power generation." In IET Seminar on Power Generation Control. IET, 2007. http://dx.doi.org/10.1049/ic.2007.1669.

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Lee, Duehee, Jinho Lee, and Ross Baldick. "Wind power scenario generation for stochastic wind power generation and transmission expansion planning." In 2014 IEEE Power & Energy Society General Meeting. IEEE, 2014. http://dx.doi.org/10.1109/pesgm.2014.6939930.

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Yong Yan, Shouhui Yang, Fushuan Wen, and I. MacGill. "Generation scheduling with volatile wind power generation." In 2009 International Conference on Sustainable Power Generation and Supply. SUPERGEN 2009. IEEE, 2009. http://dx.doi.org/10.1109/supergen.2009.5348285.

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Kalaivani, C., and K. Rajambal. "Six Phase Wind Power Generation." In 2018 4th International Conference on Electrical Energy Systems (ICEES). IEEE, 2018. http://dx.doi.org/10.1109/icees.2018.8443254.

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Wannakam, Khanittha, and Somchat Jiriwibhakorn. "Assessment of Wind Power Generation." In 2018 International Conference on Engineering, Applied Sciences, and Technology (ICEAST). IEEE, 2018. http://dx.doi.org/10.1109/iceast.2018.8434443.

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Jie Yan, Shuang Han, Furong Li, Yongqian Liu, and Chenghong Gu. "A robust probabilistic wind power forecasting method considering wind scenarios." In 3rd Renewable Power Generation Conference (RPG 2014). Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.0828.

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Borodulin, Mikhail Y. "Validation of wind turbine generator stability models for wind generation interconnection studies." In 2014 IEEE Power & Energy Society General Meeting. IEEE, 2014. http://dx.doi.org/10.1109/pesgm.2014.6938934.

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Yihong Wang, Yingdan Fan, Bin Huang, Hong Shen, and Baiyang Song. "Study on reactive power compensation of 3000 MW wind power of Jiuquan wind power base integration." In 2nd IET Renewable Power Generation Conference (RPG 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.1809.

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Shuang Han, Xiaomei Ma, Jie Yan, and Yongqian Liu. "Research on fitting method of wind shear exponent in wind farm." In 3rd Renewable Power Generation Conference (RPG 2014). Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.0818.

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Reports on the topic "Wind power generation"

1

Muljadi, E., Y. C. Zhang, A. Allen, M. Singh, V. Gevorgian, and Y. H. Wan. Synchrophasor Applications for Wind Power Generation. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1126317.

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Ndoye, M., and C. Kamath. Understanding Diurnal Patterns in Wind Power Generation Data. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1107316.

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Sezgen, O., C. Marnay, and S. Bretz. Wind Generation in the Future Competitive California Power Market. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/6467.

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Constantinescu, E. M., V. M. Zavala, M. Rocklin, S. Lee, and M. Anitescu. Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/1009334.

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Lacommare, Kristina S. H. Power and Frequency Control as it Relates to Wind-Powered Generation. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/1003828.

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Vittal, Vijay, Gerald T. Heydt, Raja Ayyanar, James D. McCalley, V. Ajjarapu, and Dionysios Aliprantis. Topic 5: Power System Operation and Planning for Enhanced Wind Generation Penetration. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1063632.

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Makarov, Yuri V., Zhenyu Huang, Pavel V. Etingov, et al. Wind Energy Management System Integration Project Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/985583.

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Makarov, Yuri V., Zhenyu Huang, Pavel V. Etingov, et al. Wind Energy Management System EMS Integration Project: Incorporating Wind Generation and Load Forecast Uncertainties into Power Grid Operations. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/977321.

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Porter, K., S. Fink, C. Mudd, and J. DeCesaro. Generation Interconnection Policies and Wind Power: A Discussion of Issues, Problems, and Potential Solutions. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/1218406.

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Rosalie, Ruegg, and Patrick Thomas. Linkages from DOE's Wind Energy Program R&D to Commercial Renewable Power Generation. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/1219919.

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