Wind Turbine Power Curve Modeling Using Advanced Parametric and Nonparametric Methods
Wind turbine power curve modeling is an important tool in turbine performance monitoring and power forecasting. There are several statistical techniques to fit the empirical power curve of a wind turbine, which can be classified into parametric and nonparametric methods. In this paper, we study four...
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| Veröffentlicht in: | IEEE transactions on sustainable energy 2014-10, Vol.5 (4), p.1262-1269 |
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| creator | Shokrzadeh, Shahab Jafari Jozani, Mohammad Bibeau, Eric |
| description | Wind turbine power curve modeling is an important tool in turbine performance monitoring and power forecasting. There are several statistical techniques to fit the empirical power curve of a wind turbine, which can be classified into parametric and nonparametric methods. In this paper, we study four of these methods to estimate the wind turbine power curve. Polynomial regression is studied as the benchmark parametric model, and issues associated with this technique are discussed. We then introduce the locally weighted polynomial regression method, and show its advantages over the polynomial regression. Also, the spline regression method is examined to achieve more flexibility for fitting the power curve. Finally, we develop a penalized spline regression model to address the issues of choosing the number and location of knots in the spline regression. The performance of the presented methods is evaluated using two simulated data sets as well as an actual operational power data of a wind farm in North America. |
| doi_str_mv | 10.1109/TSTE.2014.2345059 |
| format | Article |
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There are several statistical techniques to fit the empirical power curve of a wind turbine, which can be classified into parametric and nonparametric methods. In this paper, we study four of these methods to estimate the wind turbine power curve. Polynomial regression is studied as the benchmark parametric model, and issues associated with this technique are discussed. We then introduce the locally weighted polynomial regression method, and show its advantages over the polynomial regression. Also, the spline regression method is examined to achieve more flexibility for fitting the power curve. Finally, we develop a penalized spline regression model to address the issues of choosing the number and location of knots in the spline regression. 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There are several statistical techniques to fit the empirical power curve of a wind turbine, which can be classified into parametric and nonparametric methods. In this paper, we study four of these methods to estimate the wind turbine power curve. Polynomial regression is studied as the benchmark parametric model, and issues associated with this technique are discussed. We then introduce the locally weighted polynomial regression method, and show its advantages over the polynomial regression. Also, the spline regression method is examined to achieve more flexibility for fitting the power curve. Finally, we develop a penalized spline regression model to address the issues of choosing the number and location of knots in the spline regression. The performance of the presented methods is evaluated using two simulated data sets as well as an actual operational power data of a wind farm in North America.</description><subject>Data models</subject><subject>Methods</subject><subject>Nonparametric regression</subject><subject>penalized spline regression</subject><subject>polynomial regression</subject><subject>Polynomials</subject><subject>Regression analysis</subject><subject>Splines (mathematics)</subject><subject>Turbines</subject><subject>Wind energy</subject><subject>Wind power generation</subject><subject>wind turbine power curve</subject><subject>Wind turbines</subject><issn>1949-3029</issn><issn>1949-3037</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><recordid>eNpFkE1rwzAMhs3YYKXrDxi7GHZOZ1txHB9L6T6g3QpL2NE4ibKltElnJx3790to6XSQhHhfSTyE3HI25Zzph-Q9WUwF4-FUQCiZ1BdkxHWoA2CgLs-90Ndk4v2G9QEAEbARST-quqBJ57KqRrpuftDReecOSFdNgduq_qSpH_KsONg6x4KurbM7bF2VU9tbX5t6_z9ZYfvVFP6GXJV263FyqmOSPi6S-XOwfHt6mc-WQQ5x2AYFlLJ_y4K2mcL-76iQlmnJmLJWIc9EpEFIKCMpIwZcAFdlrATaLI9iZWFM7o9796757tC3ZtN0ru5PGq6kDmMJse5V_KjKXeO9w9LsXbWz7tdwZgaAZgBoBoDmBLD33B09FSKe9VGsQwES_gCpBWrP</recordid><startdate>20141001</startdate><enddate>20141001</enddate><creator>Shokrzadeh, Shahab</creator><creator>Jafari Jozani, Mohammad</creator><creator>Bibeau, Eric</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| source | IEEE Electronic Library (IEL) |
| subjects | Data models Methods Nonparametric regression penalized spline regression polynomial regression Polynomials Regression analysis Splines (mathematics) Turbines Wind energy Wind power generation wind turbine power curve Wind turbines |
| title | Wind Turbine Power Curve Modeling Using Advanced Parametric and Nonparametric Methods |
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