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Extended Debye model parameter identification method based on shuffled frog-leaping particle swarm optimization

A particle swarm algorithm and extended Debye technology, applied in the field of effective circuit model parameter identification, can solve problems such as easy to fall into local optimum and single learning object, and achieve the effect of improving algorithm diversity and enhancing global optimization ability

Pending Publication Date: 2022-03-25
STATE GRID PUTIAN ELECTRIC POWER SUPPLY +1
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Problems solved by technology

Compared with the locust algorithm and the chicken swarm algorithm, the particle swarm algorithm has the advantages of fast search speed and simple programming. However, the learning object of the algorithm is single in the later stage, and it is easy to fall into local optimum

Method used

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  • Extended Debye model parameter identification method based on shuffled frog-leaping particle swarm optimization
  • Extended Debye model parameter identification method based on shuffled frog-leaping particle swarm optimization
  • Extended Debye model parameter identification method based on shuffled frog-leaping particle swarm optimization

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Embodiment 1

[0044] see figure 1 , this embodiment proposes an extended Debye model parameter identification method based on the hybrid leapfrog particle swarm algorithm, including the following steps:

[0045] Establish an extended Debye model, the extended Debye model established in this embodiment is as follows figure 2 As shown; the oil-paper insulation system contains a variety of dielectric response processes under the action of an alternating electric field, and its conductance and polarization responses will change as the frequency of the electric field changes. The extended Debye model characterizes the complex dielectric process of oil-paper insulation by constructing RC parallel geometric equivalent circuits and N RC series polarization equivalent circuits. where C g is the lossless polarization equivalent capacitance, Rg is the insulation resistance, Rpi represents the energy loss of the i-th relaxed polarization branch, and Cpi represents the capacitance value of the i-th r...

Embodiment 2

[0091] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored on the memory and operable on the processor. It is characterized in that, when the processor executes the program, any implementation of the present invention The parameter identification method of the extended Debye model described in the example.

Embodiment 3

[0093] This embodiment provides a computer-readable storage medium, on which a computer program is stored, wherein when the program is executed by a processor, the extended Debye model parameter identification method as described in any embodiment of the present invention is implemented.

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Abstract

The invention relates to an extended Debye model parameter identification method based on a shuffled frog-leaping particle swarm algorithm, and the method comprises the following steps: building an extended Debye model, building a calculation formula of a to-be-identified parameter, and determining a target function; initializing a particle swarm to obtain n particles corresponding to n-dimensional solution vectors of the extended Debye model, and calculating the self-fitness of each particle according to the calculation formula of the to-be-identified parameters and the target function; introducing difference grouping of a leapfrog algorithm, and dividing the n particles into m ethnic groups according to the self-fitness of each particle; iterating and optimizing each ethnic group, outputting the position of a particle with a global optimal value as a model identification parameter of the extended Debye model after an iteration termination condition is reached, and adding difference grouping of a leapfrog algorithm into a traditional particle swarm algorithm, thereby preventing particle swarms from being concentrated in the same direction too early, and improving the robustness of the extended Debye model. And the mutual learning ability of particles in the same group is also improved.

Description

technical field [0001] The invention relates to an extended Debye model parameter identification method based on a hybrid leapfrog particle swarm algorithm, and belongs to the technical field of transformer equivalent circuit model parameter identification. Background technique [0002] The extended Debye model is a classic equivalent circuit model of oil-paper insulation system, and its model parameters are of great significance for analyzing the internal micro-dielectric response of oil-paper insulation. At present, most of the parameter identification methods of the extended Debye model adopt intelligent algorithm identification. Particle swarm optimization algorithm is proposed based on the flock of birds foraging. During the whole search process, the flock of birds transmits the information of their respective positions to each other, so that the whole flock of birds can gather around the food source. There are two main factors affecting the foraging direction of birds...

Claims

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Application Information

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IPC IPC(8): G06F30/3323G06N3/00
CPCG06F30/3323G06N3/006
Inventor 林明星陈扩松林翊乾郑宇李语菲潘亦斌
Owner STATE GRID PUTIAN ELECTRIC POWER SUPPLY
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