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Load characteristics comprehensive classification method based on Markov Monte Carlo

A classification method and a technology of load characteristics, which are applied in the directions of instruments, calculations, character and pattern recognition, etc., can solve the problems that cannot consider the randomness and time-varying nature of loads, etc.

Active Publication Date: 2015-12-30
SHANDONG UNIV
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Problems solved by technology

[0004] In order to solve the deficiencies in the prior art, the present invention discloses a classification feature aimed at the randomness and time-varying nature of electric load, and provides a high-accuracy classification and synthesis method aimed at the randomness and time-varying nature of electric load, which overcomes the Existing load modeling methods cannot consider the randomness and time-varying nature of load

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  • Load characteristics comprehensive classification method based on Markov Monte Carlo
  • Load characteristics comprehensive classification method based on Markov Monte Carlo
  • Load characteristics comprehensive classification method based on Markov Monte Carlo

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Embodiment Construction

[0088] The present invention is described in detail below in conjunction with accompanying drawing:

[0089] Such as figure 2 As shown, execute step 01 to start;

[0090] Secondly, execute step 02 to calculate statistics This statistic has (n-1) degrees of freedom 2 χ of 2 distributed.

[0091] let m ij means X (0) (t) The frequency of transition from state i to state j in one step, and divide the sum of each column of the transition frequency matrix by the sum of each row and each column, and the obtained value is denoted as P' j :

[0092] P ′ j = Σ j = 1 m m i j Σ i = ...

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Abstract

The invention discloses a load characteristics comprehensive classification method based on Markov Monte Carlo. The method includes the following steps: finding the voltage drop time point, carrying out load dynamic characteristics extraction and classification at the disturbance moment corresponding to the voltage drop time point; judging whether the change between the load classifications has a Markov property or not; dividing all data into uniform segments by time; establishing Markov chain's probability transfer matrix based on the maximum likelihood thought for each data segment; judging whether the numerical characteristics are changed or not: if no, go to step V; if yes, carrying out clustering on the load data in the time segment according to the numerical characteristics corresponding to the matrix, and obtaining the probability transfer matrix of the load data with changed numerical characteristics in each time segment ; carrying out Markov Monte Carlo simulation and describing the load change situation; processing the sequence reflecting the load classification conversion using the Hidden Markov Model (HMM). The method provided by the invention improves the Markov chain Monte Carlo simulation and effectively reduces the possibility of the matrix entering the stable state after iteration.

Description

technical field [0001] The invention relates to a method for classification and synthesis of load characteristics based on Markov Monte Carlo. Background technique [0002] Load modeling is a fundamental and key issue in power system modeling. Establishing a load model that can accurately reflect the load characteristics has always been a challenging and difficult problem. The biggest difficulty in load modeling lies in the randomness and time-varying nature of the load, which includes changes in the magnitude of the load and changes in the components of the load. Even so, there are certain regularities in the load characteristics. [0003] In order to solve the random and time-varying problems of load composition on the basis of grasping the laws, the dynamic characteristics of loads are classified and synthesized. The classification and synthesis of load dynamic characteristics is to classify the load components in the dynamic load characteristic data of the same substat...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/241G06F18/295
Inventor 王振树周光耀
Owner SHANDONG UNIV
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