Wind power curve fitting method based on sparse heteroscedasticity multi-strip regression

A curve fitting and heteroscedasticity technology, applied in the field of new energy and statistics, which can solve the problems of low power curve fitting accuracy and large error, etc.

Active Publication Date: 2020-04-17
CENT SOUTH UNIV
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Problems solved by technology

[0007] The present invention provides a wind power curve fitting method based on sparse heteroscedasticity multiple regression, the purpose of which is to solve the two major defects of the existing power curve fitting, resulting in low power curve fitting accuracy and large errors question

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[0071] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

[0072] Aiming at the two major defects of the existing power curve fitting, which lead to the problem of low power curve fitting accuracy and large error, the present invention provides a wind power curve fitting method based on sparse heteroscedasticity multiple regression.

[0073] Such as Figure 4 As shown, the embodiment of the present invention provides a method of wind power curve fitting based on sparse heteroscedasticity multivariate regression, including:

[0074] Step 1, using the fuzzy C-means algorithm to automatically detect abnormal points, and obtain data to remove abnormal points for the original wind power data;

[0075] Step 2, constructing a sparse heteroscedastic multivariate regression model based on the acquired data;

[0076] St...

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Abstract

The invention provides a wind power curve fitting method based on sparse heteroscedasticity multi-strip regression, and the method comprises the steps: automatically detecting an abnormal point through employing a fuzzy C-means algorithm, and obtaining the data of which the abnormal point is removed for original wind power data; constructing a sparse heteroscedasticity multi-strip regression modelaccording to the obtained data; optimizing the constructed sparse heteroscedasticity multi-strip regression model by adopting a variational Bayesian method to obtain posteriori distribution conditions and parameter formulas of all parameters in the model; and initializing model parameters, and solving estimated values of the parameters by utilizing an iterative method according to posteriori distribution conditions and parameter formulas of all the parameters in the model. According to the wind power curve fitting method based on sparse heteroscedasticity multi-spline regression provided by the invention, a plurality of spline basis functions is integrated, the nonlinear fitting capability of the model is improved, and the influence of redundant information on a final regression result isavoided.

Description

technical field [0001] The invention relates to the fields of new energy and statistics, in particular to a method for fitting wind power curves based on sparse heteroscedasticity multivariate regression. Background technique [0002] The development and utilization of new energy has become an important way to solve the world's energy shortage and environmental pollution problems. As a clean, environmentally friendly and inexhaustible renewable energy, wind energy has received more and more attention. An accurate wind power curve plays an important role in the wide application of wind energy. [0003] Usually, the fan manufacturers provide corresponding theoretical power curves for the fans they produce. These theoretical power curves are generally obtained at a fixed air density. However, climatic conditions change over time and geographically. Therefore, the performance of the same wind turbine varies in different seasons and in different wind fields. Therefore, it is...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/20G06K9/62G06N7/00G06F113/06
CPCG06N7/01G06F18/23213G06F18/2433
Inventor 汪运邹润民李意芬杨佳欣
Owner CENT SOUTH UNIV
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