Low-sensitively ensemble Kalman filtering-based induction motor state monitoring method

A Kalman filter, induction motor technology, applied in motor generator testing, measuring electricity, measuring devices, etc., can solve the problem of low accuracy of condition monitoring results

Active Publication Date: 2019-01-25
ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
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Problems solved by technology

[0003] The technical problem to be solved by the present invention is to provide a state monitoring method for induction motors based on weakly sensi

Method used

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  • Low-sensitively ensemble Kalman filtering-based induction motor state monitoring method

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Embodiment 1

[0081] Embodiment 1: A method for state monitoring of an induction motor based on a weakly sensitive ensemble Kalman filter, the flow chart of which is shown in figure 1 , the schematic diagram of weakly sensitive ensemble Kalman filter see figure 2 ,Proceed as follows:

[0082] Step (1): Establish the nonlinear state equation of the induction motor system

[0083] In an induction motor system, take the state vector x=[x 1 ,x 2 ,x 3 ,x 4 ,x 5 ] T , then its state equation is:

[0084]

[0085] where x 1 and x 2 is the stator current, x 3 and x 4 is the rotor flux linkage, x 5 is the angular velocity; K is a fixed parameter; λ is the reciprocal of the instantaneous time constant; J is the rotor inertia; p is the number of pole pairs; u 1 and u 2 is the stator voltage control input; c=[c 1 c 2 ], is an uncertain parameter vector, c 1 and c 2 are rotor resistance and stator resistance respectively; w is zero mean Gaussian white noise; other model parameters...

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Abstract

The invention discloses a low-sensitively ensemble Kalman filtering-based induction motor state monitoring method. The invention aims to solve the technical problem of the low precision of an induction motor monitoring result obtained by an existing method. The method includes the following steps that: the state equation of an induction motor system is established; the measurement equation of theinduction motor system is established; the state equation and measurement equation are a discretized; and low-sensitively ensemble Kalman filtering is performed on the discretized state equation and measurement equation, so that the stator current, rotor flux and angular velocity of the induction motor are outputted. Compared with EnKF, the low-sensitively ensemble Kalman filtering-based inductionmotor state monitoring method of the invention adopts a low-sensitivity optimal control technology to eliminate the uncertainty of parameters (such as rotor resistance and stator resistance) in the induction motor system, so as to decrease the sensitivity of the estimation of the states (stator current, rotor flux and angular velocity) of the induction motor system to uncertain parameters, and improve the state monitoring accuracy of the induction motor. The method can be applied to the state PID control process of the induction motor.

Description

technical field [0001] The invention relates to the technical field of induction motor state monitoring, in particular to an induction motor state monitoring method based on weakly sensitive set Kalman filter. Background technique [0002] Due to its simple structure, stable performance, low cost, and convenient manufacturing, induction motors have attracted extensive attention in theoretical research and practical applications, and are widely used in electric vehicles, transportation, and CNC machine tools. At present, the practical induction motor AC speed control system generally uses indirect methods to monitor the flux linkage and speed, that is, by detecting the easily measurable physical quantities such as the voltage and current at the stator end of the motor, and using the state estimation method to calculate the flux linkage and speed in real time. So as to realize the precise control of flux linkage and speed, that is, the so-called speed sensorless control of ind...

Claims

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Application Information

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IPC IPC(8): G01R31/34G06F17/12G06F17/16
CPCG01R31/343G01R31/346G06F17/12G06F17/16
Inventor 娄泰山陈南华杨存祥杨小亮丁国强王妍凌丹王延峰王磊张云玲
Owner ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
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