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Markov chain modeling and predicating method based on wind power variable quantity

A markov chain, wind power technology, used in forecasting, data processing applications, instruments, etc.

Inactive Publication Date: 2015-03-25
SHANDONG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

The above literature only conducts statistical research on the distribution characteristics of the variation, and does not use the variation data for further modeling applications

Method used

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  • Markov chain modeling and predicating method based on wind power variable quantity
  • Markov chain modeling and predicating method based on wind power variable quantity
  • Markov chain modeling and predicating method based on wind power variable quantity

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Embodiment Construction

[0128] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0129] 1 Markov chain model based on wind power variation

[0130] 1.1 Discrete Markov chain model

[0131] Both time and state are discrete random processes {X n =X(n), the state space of n=0,1,2,...} is I={S 1 ,S 2 ,...}. Assume that as long as the process is in state S at the current moment i , there is a fixed probability that the process will be in state S at the next moment j , that is, assuming that for all states and all n≥0, we have

[0132] P{X n = S j |X 1 = S 1 ,X 2 = S 2 ,…X n-1 = S i}

[0133] =P{X n = S j |X n-1 = S i},S · ∈I (1)

[0134] Such random processes are called Markov chains. For a Markov chain, in a given past state S 0 , S 1 ,...,S n-1 and the current state S n , the future state X n+1 The conditional distribution of is independent of past states and only depends on the present state S n . Denote t...

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Abstract

The invention discloses a Markov chain modeling and predicating method based on wind power variable quantity. The method comprises the steps that firstly, linear transformation is carried out on existing original power data to obtain a wind power variable quantity data sample, then, according to the variable quantity data size and a statistical result of probability distribution, state space of a Markov chain model is divided as fine as possible, after the state is determined, a transition probability matrix of the variable quantity is obtained through statistical calculation, and Markov chain model construction is completed. The Markov chain model can be used for constructing a short-period and ultra-short period wind power predication method, and the theoretical basis is laid for real-time economic dispatch comprising a wind power system, optimized decision and model prediction control based on a Markov chain.

Description

technical field [0001] The invention relates to a Markov chain modeling and prediction method based on wind power variation. Background technique [0002] With the increasingly prominent energy and environmental issues, wind power has developed rapidly due to its clean, renewable, and huge reserves. According to the latest statistics from the China Wind Energy Association, in 2013, China (excluding Taiwan) newly added installed capacity of 16,088.7MW, a year-on-year increase of 24.1%; cumulative installed capacity of 91,412.89MW, a year-on-year increase of 21.4%. Both new installed capacity and cumulative installed capacity rank first in the world. Although the wind power generation technology continues to mature, the randomness, volatility and uncontrollability of wind power output still bring many problems to the large-scale grid connection of wind power. Therefore, it is of great significance to accurately model the fluctuation characteristics of wind power to realize t...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 贠志皓孙景文
Owner SHANDONG UNIV
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