Non-gaussian system dynamic data correction and system control performance optimization method
A technology of system dynamics and data correction, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve problems such as the control strategy optimization effect is not obvious, not close enough to the real value, etc.
Active Publication Date: 2019-01-18
TAIYUAN UNIV OF TECH
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Problems solved by technology
[0006] The technical problem solved by the present invention is: in the case of the wind power generation process being affected by non-Gaussian process noise (random wind speed) and non-Gaussian measurement noise, the data measurement correction is not close enough to the real value, and the control strategy optimization effect is not obvious. Provide a non-Gaussian system dynamic data correction and system control performance optimization method
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[0077] The method of the present invention is described below in conjunction with specific examples: for a typical wind energy conversion system, the equivalent load of the permanent magnet synchronous generator is determined by the constant inductance L s and variable resistor R s Composed in parallel. In this application example, the non-Gaussian system dynamic data correction and system control performance optimization method includes the following steps:
[0078] Step 1. The permanent magnet synchronous generator model can be described as:
[0079]
[0080] where R is the stator resistance, u d and u q are the voltages of the d component and q component of the stator respectively, L d and L q are the inductances of the d and q components of the stator, respectively, i d and i q are the currents of the d component and q component of the stator respectively, φ m is the magnetic flux (it is a constant), p is the number of pole pairs, Ω G is the rotational speed of...
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The invention relates to a system control performance optimization method, in particular to a non-gaussian system dynamic data correction and system control performance optimization method, and the problem that when the wind power generation process is affected by non-gaussian process noise and non-gaussian measurement noise, the data is not close enough to the true value after the data measurement is corrected, and the optimization effect of the control strategy is not obvious is solved; the method comprises the following steps of firstly, describing a system model under the non-gaussian disturbance; secondly, performing derivation of an iteration formula in the problem description by utilizing an EM algorithm; thirdly, solving the corrected output, a formula which is as shown in the specification, by utilizing the iterative formula; and fourthly, performing selecting based on the performance indexes of the statistical information to obtain an optimal control law. The method has the advantages that 1, influence of non-gaussian random noise on the system is considered; 2, dynamic characteristics of the process are considered, and the actual process is better expressed; 3, relatively high calculation efficiency is achieved, and the requirements of the actual industrial process are met; and 4, the random characteristics of the non-gaussian random quantity are fully described by adopting entropy statistical information, and a tracking control index is established.
Description
technical field [0001] The invention relates to a system control performance optimization method, in particular to a non-Gaussian system dynamic data correction and system control performance optimization method. Background technique [0002] In practical systems, random disturbances exist widely, especially in industrial processes, such as random factors of wind speed changes in wind power systems, and random factors of measurement errors in sensor measurement data. [0003] At present, the correction of the measurement noise of the control system is based on the assumption that it is Gaussian, and is optimized by controlling the mean value and variance of the corrected data. However, the actual industrial process measurement error is often non-Gaussian. If the traditional method under the Gaussian assumption is used for correction, the effect of optimal control is not obvious. [0004] For a control strategy to achieve a good control effect, it is very important to obtain...
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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 任密蜂张雯陈荣辉张旭霞梁艳
Owner TAIYUAN UNIV OF TECH