Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Reconstructing long-term wind data at an offshore met-mast location using cyclostationary empirical orthogonal functions
Abstract For the development of a wind power plant, plant design and its project feasibility analysis are implemented with wind data observed by a met-mast at a target location. Since observation period/time of a met-mast is normally about one year before the plant design, correlation with long-term data existing in the neighborhood of the target location is used for hindcasting past met-mast data to reduce uncertainty in the feasibility analysis, which is called the Measure-Correlate-Predict (MCP) method. In this study, cyclostationary empirical orthogonal function (CSEOF) analysis as a new approach is employed to extend the 1.5-year offshore met-mast HeMOSU-1 data into 34-year long-term data based on the MERRA reanalysis dataset. Both the one- and two-dimensional CSEOF results are compared with that of the widely-used MCP method. The CSEOF method shows a similar level of accuracy to the existing method for mean wind speed, while the former exhibits a slightly better accuracy for the frequency distribution of wind speed and the capacity factor as an index related to the estimation of wind power generation. In additional hypothetical test based on reanalysis datasets, the 1D-CSEOF method shows, in general, a better performance than the conventional MCP method in terms of the accuracy of statistical properties of wind.
Highlights The CSEOF analysis as a new approach is employed to extend the wind measurement data. The proposed method is compared with the classical MCP method. The proposed method exhibits a slightly better accuracy for the frequency distribution of wind speed.
Reconstructing long-term wind data at an offshore met-mast location using cyclostationary empirical orthogonal functions
Abstract For the development of a wind power plant, plant design and its project feasibility analysis are implemented with wind data observed by a met-mast at a target location. Since observation period/time of a met-mast is normally about one year before the plant design, correlation with long-term data existing in the neighborhood of the target location is used for hindcasting past met-mast data to reduce uncertainty in the feasibility analysis, which is called the Measure-Correlate-Predict (MCP) method. In this study, cyclostationary empirical orthogonal function (CSEOF) analysis as a new approach is employed to extend the 1.5-year offshore met-mast HeMOSU-1 data into 34-year long-term data based on the MERRA reanalysis dataset. Both the one- and two-dimensional CSEOF results are compared with that of the widely-used MCP method. The CSEOF method shows a similar level of accuracy to the existing method for mean wind speed, while the former exhibits a slightly better accuracy for the frequency distribution of wind speed and the capacity factor as an index related to the estimation of wind power generation. In additional hypothetical test based on reanalysis datasets, the 1D-CSEOF method shows, in general, a better performance than the conventional MCP method in terms of the accuracy of statistical properties of wind.
Highlights The CSEOF analysis as a new approach is employed to extend the wind measurement data. The proposed method is compared with the classical MCP method. The proposed method exhibits a slightly better accuracy for the frequency distribution of wind speed.
Reconstructing long-term wind data at an offshore met-mast location using cyclostationary empirical orthogonal functions
Kim, Ji-Young (Autor:in) / Kim, Kwang-Yul (Autor:in)
Journal of Wind Engineering and Industrial Aerodynamics ; 156 ; 146-158
28.07.2016
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch