Method for simulation generation of wind power times series based on improved Markov chain

A Markov chain and wind power technology, which is applied in the field of simulated wind power time series generation based on improved Markov chain, and can solve problems such as the generation method of simulated wind power time series that has not been seen before.

Inactive Publication Date: 2017-08-29
NORTHEAST DIANLI UNIVERSITY
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  • Method for simulation generation of wind power times series based on improved Markov chain
  • Method for simulation generation of wind power times series based on improved Markov chain
  • Method for simulation generation of wind power times series based on improved Markov chain

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Embodiment Construction

[0060] refer to figure 1 , the present invention's simulation wind power time series generation method based on improved Markov chain, comprises the following steps:

[0061] 1) Data classification

[0062] It is necessary to comprehensively consider the seasonal characteristics, daily characteristics and fluctuation characteristics of wind power. Therefore, the historical data must be classified before calculating the state transition matrix. The classification principles are as follows:

[0063] ①Consider seasonal characteristics

[0064] The seasonal characteristics of wind power are mainly manifested in the differences in the output power in different months of the year. In order to reflect the seasonal characteristics in the generated wind power time series, it is necessary to divide the historical wind power time series with a length of one year into 12 fragments, represented by λ, λ=1, 2, ..., 12, each month is a fragment;

[0065] ②Consider daily characteristics

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Abstract

The method for simulation generation of wind power times series based on an improved Markov chain comprises: dividing historical wind power time series into different data slots according to different months and time frames, and calculating the state-transition matrix of each data slot; fitting the probability distribution of the historical wind power fluctuation quantity, and generating a set of random numbers satisfying the distribution; taking a historical wind power average value as a zero moment value prior to the beginning of simulation of the wind power series, in the calculated state-transition matrixes of the improved Markov chain model, according to the months and time frames of the previous time of the simulation of the wind power, determining corresponding state-transition matrixes, generating accumulative state-transition matrixes, and obtaining the state of the wind power at the current moment, namely an interval where the wind power is located; and extracting the fluctuation quantity, overlapping the fluctuation quantity on the wind power value of the previous time as the wind power value of the current moment, and generating the wind power value at the next moment in a similar way until generating the wind power time series requiring the number of data.

Description

technical field [0001] The invention relates to the field of wind power time series simulation in wind power grid-connected planning, and is a method for generating time series of simulated wind power based on an improved Markov chain. Background technique [0002] The randomness and uncertainty of wind power generation make the stable, safe and reliable operation of the power system face great challenges after wind power is connected to the grid. Improving the accuracy of wind power simulation sequences is of great significance in the fields of power system planning and safety assessment. [0003] The simulation generation of wind power time series refers to the generation of multiple new sequences that are consistent with the historical wind power time series in terms of statistical characteristics based on the historical wind power time series. [0004] The methods for generating wind power time series can be divided into two categories: wind speed method and wind power m...

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/06G06F17/50
CPCG06F30/20G06Q10/067G06Q50/06
Inventor 肖白赵宇姜卓
Owner NORTHEAST DIANLI UNIVERSITY
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