Current Issue : April-June Volume : 2022 Issue Number : 2 Articles : 5 Articles
Typhoon wind speed prediction is of great significance for it can help prevent wind farms from damages caused by frequent typhoon disasters in coastal areas. However, most researches on wind forecast are either for meteorological application or for normal weather. -erefore, this paper proposes a systematic method based on numerical wind field and extreme learning machine for typhoon wind speed prediction of wind farms. -e proposed method mainly consists of three parts, IGA-YanMeng typhoon numerical simulation model, typhoon status prediction model, and wind speed simulation model based on an extreme learning machine. -e IGA-YanMeng typhoon numerical simulation model can greatly enrich typhoon wind speed data according to historical typhoon parameters. -e typhoon status prediction model can predict the status of typhoons studied in the next few hours. And wind speed simulation model simulates the average wind speed magnitude/direction at 10m height of each turbine in the farm according to the predicted status. -e end of this paper presents a case study on a wind farm located in Guangdong province that suffered from the super typhoon Mangkhut landed in 2018. -e results verified the feasibility and effectiveness of the proposed method....
To enable power generation companies to make full use of effective wind energy resources and grid companies to correctly schedule wind power, this paper proposes a model of offshore wind power forecast considering the variation of wind speed in second-level time scale. First, data preprocessing is utilized to process the abnormal data and complete the normalization of offshore wind speed and wind power. )en, a wind speed prediction model is established in the second time scale through the differential smoothing power sequence. Finally, a rolling PSO-LSTM memory network is authorized to realize the prediction of second-level time scale wind speed and power. An offshore wind power case is utilized to illustrate and characterize the performance of the wind power forecast model....
In the present work, a methodology that allows optimizing the permanent magnet synchronous generator (PMSG) design by establishing limit values of magnet radius and length that maximize efficiency for the nominal parameters of the wind turbine is developed. The methodology consists of two fundamental models. One model calculates the generator parameters from the radius of the magnet base, and the other optimization model determines two optimum generators according to the optimization criteria of maximum efficiency and maximum efficiency with minimum weight starting from the axial length and the radius of the magnet base. For the optimization, the numerical method of the golden section was used. The model was validated from a 10 kW PMSG and the results of two optimum generators are presented according to the optimization criteria. In addition, when the obtained results are compared with the reference electric generator, an increase in efficiency of 1.15% and 0.81% and a reduction in weight of 30.79% and 39.15% of the optimized generators are obtained for maximum efficiency and minimum weight, respectively. Intermediate options between the maximum efficiency generator and the minimum weight generator allows for the selection of the optimum dimensioning for the electric generator as a function of the parameters from the wind turbine design....
Europe’s initiative to reduce the emissions of harmful gases has significantly increased the integration of renewable sources into power networks, particularly wind power. Variable renewable sources pose challenges to sustain the balance between generation and demand. Thus, the need for ancillary services to cope with this problem has increased. In this regard, the integration of larger shares of wind generation would have a clear system benefit when wind generators are able to provide these ancillary services. This would also have implications for electricity markets, enabling these services from wind power plants. This article gives an overview of several European markets for frequency support (FS) services, also referred to as FS markets . It identifies the changes in national regulations of 10 European countries to standardize these services based on the ENTSO-E guidelines. However, most of the countries still use their national service definitions, which presents a problem for researchers to understand the national regulations in relation to the ENTSO-E guidelines. This article provides a classification of the national FS services under the definitions of the ENTSO-E guidelines to facilitate research on this topic. Furthermore, it highlights the main requirements for the market practices that would encourage the participation of wind power generation in the provision of these services. An estimation of the economic benefits for wind producers from the provision of FS services is provided as well to show a possible outcome if changes are not made in national policies....
Before a new wind farm can be built, politics and regional planning must approve of the respective area as a suitable site. For this purpose, large-scale potential computations were carried out to identify suitable areas. The calculation of wind power plant potential usually focuses on capturing the highest energy potential. In Germany, due to an energy production reimbursement factor defined in the Renewable Energy Sources Act (“Erneuerbare-Energien-Gesetz”, EEG) in 2017, the influence of energy quantities on the power plant potential varies, economically and spatially. Therefore, in addition to the calculation of energy potentials, it was also necessary to perform a potential analysis in terms of economic efficiency. This allows, on the one hand, an economic review of the areas tendered by the regional planning and, on the other hand, a spatial-economic analysis that expands the parameters in the search for new areas. In this work, (a) potentials with regard to the levelized cost of electricity (LCOE) were calculated by the example of the electricity market in Germany, which were then (b) spatially and statistically processed on the level of the federal states....
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