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Nonlinear regression tuning method of synchronous machine model parameters based on penalty factor

A non-linear regression and synchronous motor technology, applied in electrical digital data processing, design optimization/simulation, special data processing applications, etc., can solve problems such as inability to obtain the best results and inaccurate calculation results

Inactive Publication Date: 2018-12-07
CENT SOUTH UNIV
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to provide a non-linear regression tuning method for motor model parameters to solve the problems and deficiencies that the calculation results of the existing methods proposed in the above-mentioned background technology are not accurate enough and the best results cannot be obtained

Method used

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  • Nonlinear regression tuning method of synchronous machine model parameters based on penalty factor
  • Nonlinear regression tuning method of synchronous machine model parameters based on penalty factor
  • Nonlinear regression tuning method of synchronous machine model parameters based on penalty factor

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Experimental program
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Embodiment approach

[0041] 1. The motor model parameter nonlinear regression tuning method, the motor model parameter nonlinear regression tuning method; includes the following steps: establishing a mathematical model, extracting key data points of peak and valley points, nonlinear regression problem, large residual error The nonlinear parametric regression solution of ;

[0042] Establish a mathematical model; establish a mathematical relationship between parameters and output, that is, a model, for the nonlinear system to be analyzed; unify the upper and lower peak and valley points of the motor test curve into a mathematical expression;

[0043] Synchronous Motor Sudden Short Circuit Model in:

[0044] upper envelope point

[0045] lower envelope point

[0046] If the upper and lower envelope points appear alternately, the upper and lower points are unified as

[0047]

[0048] , where if the starting point is the upper envelope, then k=0; otherwise k=1, j is the jth envelope point...

example

[0067] Example: Synchronous motor sudden short circuit model, here t=0.01*i+0.00386, 2πft+0.358 radians=180*i+90 degrees, 6.546 is the steady state current, here sin(2πf(0.01*i+0.00386)+0.358)= (-1) i , represents the peak-to-valley point of the short-circuit current of the synchronous motor; here p(5)=p'(5)sin(0.358), 0.358 is the initial angle at the closing moment, f=50Hz, 2f=100, therefore, 0.386*π+0.358 = π / 2 radians, that is, 90 degrees;

[0068] , plus noise Number of calculation cycles = 30.

[0069] Table 1 Comparison of LM algorithm and improved algorithm results

[0070]

[0071] Note: The data is generated by synchronous motor model value plus noise 0.1randn(), Ptrue represents the real parameter value of the model, Pinitial represents the initial value calculated by the algorithm, Pfit(LM) represents the calculated value fitted by the LM algorithm, Pfit(μ=0.103,α) Indicates the value of the improved algorithm fitting result; the value of λ is determined ...

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Abstract

The invention provides a nonlinear regression tuning method for synchronous motor model parameters based on penalty factors. The nonlinear regression tuning method for motor model parameters comprisesthe following steps: establishing a mathematical model, extracting key data points of peak and valley points, and obtaining nonlinear regression problem and nonlinear parameter regression solutions under large residual error. The LM algorithm can be used to solve the nonlinear regression parameter problem without residual error. If there is residual error, the LM algorithm can not find the idealsolution. The invention aims at the parameter regression analysis of the sudden short circuit of the synchronous motor with residual error, the nonlinear parameter regression analysis of the sudden short circuit test of the synchronous machine is solved, the nonlinear parameter regression analysis is insensitive to the initial value when the nonlinear regression analysis is made when there is residual error, and a better fitting effect can be obtained. The method has the universality of solving all nonlinear parameter regression. Through the present invention, an engineering solution is provided to the problem of residual nonlinear parameter regression analysis.

Description

technical field [0001] The invention relates to the field of practical engineering technology, and more specifically, relates to a nonlinear regression optimization method of motor model parameters. Background technique [0002] In the field of actual engineering, the system model is often known, but the parameters in the system are unknown, and the time response of the system can be measured through experiments; the method of finding system parameters through the time response is called regression analysis; if the system model is a linear relationship, linear regression It is relatively easy to obtain; if the system model is a nonlinear relationship, especially a multi-exponential function and a nonlinear relationship, the optimal solution of the nonlinear regression is particularly difficult to obtain. [0003] In actual engineering, the system model is known, the parameters in the system are unknown, and several parameters of the system are required; the sudden short circ...

Claims

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 刘连浩张殿生畅帅
Owner CENT SOUTH UNIV
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