Wind power fluctuation probability density modeling method based on nonparametric kernel density estimation

Abstract

The present invention provides a wind power fluctuation probability density modeling method based on nonparametric kernel density estimation. The method comprises the following steps: 1, extracting a fluctuation amount of wind power sample data by wavelet decomposition; 2, establishing a corresponding nonparametric kernel density estimation model based on a fluctuation amount sample, and then aiming at the model bandwidth selection problem, constructing a constrained bandwidth optimization model which uses a goodness-of-fit test as a constraint condition; and 3, solving the optimization model by using a constrained sequence optimization algorithm. According to the present invention, due to adoption of the wavelet decomposition method, a wind power fluctuation component can be more precisely extracted; moreover, a probability characteristic modeling method of the extracted fluctuation component is entirely driven by the sample data without performing prior subjective assumption on the probability density model, so that the method has higher modeling accuracy and applicability; and an improvement strategy aiming at the nonparametric kernel density estimation method also enables modeling accuracy and computing efficiency of the method to be effectively improved.

Description

technical field [0001] The invention belongs to the field of wind power fluctuation research, and in particular relates to a wind power fluctuation extraction method and a probability density modeling method based on non-parametric kernel density estimation. Background technique [0002] In recent years, with the rapid development of my country's wind power industry, the installed capacity of wind power grid-connected has continued to grow. By the end of 2014, my country's cumulative installed capacity had reached 114.6GW. Although the large-scale grid connection of wind power can relieve environmental pressure and energy crisis to a certain extent, the fluctuation and randomness of wind power output will also reduce the reliability of power system operation and bring difficulties to power grid planning and dispatching. Therefore, it is necessary to study the volatility of wind power, grasp its inherent probability characteristics, and solve the problem of large-scale wind p...

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Problems solved by technology

In order to improve the applicability of non-parametric kernel density estimation in the specific problem of wind power fluctuation modeling, the present invention constructs a bandwidth optimization model with a goodness-of-fit test as a constraint condition, which effectively solves the problem of bandwidth selection in the process of The problem of coordination of model accuracy and smoothness, and then a constrained order optimization algorithm is proposed to solve the model, thus improving the computational efficiency of the non-parametric kernel density estimation method

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