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Load model parameter identification method based on clone selection algorithm

A clonal selection algorithm and load model technology, applied in the field of identification, can solve problems such as slow convergence speed and strong sensitivity, achieve good parallelism, optimization performance and strong robustness, and avoid mass reproduction.

Inactive Publication Date: 2014-02-12
STATE GRID CORP OF CHINA +2
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Problems solved by technology

However, the algorithm is sensitive to the selection of the initial value of the parameters, the convergence speed is relatively slow, and it is easy to fall into the local optimal solution.

Method used

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  • Load model parameter identification method based on clone selection algorithm
  • Load model parameter identification method based on clone selection algorithm
  • Load model parameter identification method based on clone selection algorithm

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[0026] A load model parameter identification method based on clonal selection algorithm, the method uses the parameter to be identified as the antigen, the objective function of the parameter as the antibody, and takes the highest affinity between the antibody and the antigen as the goal to obtain a set of optimal loads Model parameters. The specific steps are as figure 1 shown, including:

[0027] 1) Initially, the immune algorithm randomly generates immune cells corresponding to the parameters to be identified, randomly generates a population of a certain scale, and then divides the population into multiple niches;

[0028] 2) Allow immune cells to optimize selection within each niche, including crossover or mutation;

[0029] 3) Select the optimal immune cells in each niche for crossover or mutation;

[0030] 4) Judging that the affinity meets the set value, if it is satisfied, exit, and the optimal immune cell in the population is the load model parameter; otherwise, go...

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Abstract

The invention relates to a load model parameter identification method based on a clone selection algorithm. According to the load model parameter identification method based on the clone selection algorithm, parameters to be identified serve as an antigen, an objective function of the parameters serves as an antibody, the maximum affinity between the antibody and the antigen serves as the objective, and a set of optimal load model parameters are obtained. Compared with the prior art, the load model parameter identification method based on the clone selection algorithm is good in optimal performance and robustness, and has good parallelism and operability; due to the fact that searching is carried out in a whole solution space to find more optimal individuals, the phenomenon that due to the fact that individuals with high fitness are in mass propagation in the later period of evolution, and fill the whole solution space, optimization stops on a local optimal solution is avoided.

Description

technical field [0001] The invention relates to an identification method, in particular to a load model parameter identification method based on a clone selection algorithm. Background technique [0002] At present, the parameter identification of the load model mainly includes the following methods: [0003] 1. The least square method (LS) is an excellent data processing method, which determines the parameters of the model by minimizing the sum of squares of the generalized error (criterion function). However, because the least squares estimation is inconsistent and biased, in order to overcome its shortcomings, some identification methods based on the least squares method have been formed: generalized least squares method, auxiliary variable method and augmented matrix method, and the The general least square method combined with other methods includes correlation analysis-least square two-step method and stochastic approximation algorithm, and the method suitable for par...

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Application Information

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IPC IPC(8): G06N3/12
Inventor 凌平包海龙张宇柳劲松方陈刘舒艾芊
Owner STATE GRID CORP OF CHINA
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