The application of asynchronous motor drive systems is increasingly widespread, and many industrial applications (such as electric vehicles) use rotor-oriented control methods. Obtaining accurate rotor flux linkage is the key to achieving high performance control. Uncertainties such as motor parameter perturbation and measurement noise degrade the performance of the flux estimator. Therefore, it is a very practical problem to suppress the disturbance and improve the accuracy of flux estimation. In the recent decade, rotor flux estimation has been a hot topic of research. The main methods include methods based on motor models, signal analysis methods, and modern control theory methods. The signal analysis method often depends on the structural characteristics of the motor itself, so the generality is poor. Modern control theory, such as extended Kalman filter (EKF), model adaptive method (MRAS), Lyapunov stability principle, etc., has been applied in the observation of rotor flux linkage, but it is often difficult to balance both the error convergence speed and the parameter sensitivity. This aspect also faces the problem of excessive computational overhead. Motor model-based methods mainly include UI method, I-to method, and U-co method. The UI method does not involve rotation speed and rotor time constants, and has good robustness, so it has been widely used. The main drawback of the UI method is that it is sensitive to the perturbation of the stator resistance and is prone to integral drift. In recent years, domestic and foreign scholars have devoted themselves to the improvement of the integral link and the identification of the stator resistance and have gained valuable experience. This paper presents a rotor flux linkage estimator with improved UI method. Firstly, the stationary rotor flux estimation and stator resistance identification are performed in the coordinate system based on the tracking-differentiator-based current orientation. Based on this, the classical UI method estimator is improved. The improved UI method can suppress the influence of uncertainty factors such as stator resistance perturbation, measurement noise accumulation, and motor remanence on rotor flux estimation accuracy. In addition, it is assumed in this paper that the magnetic circuit is linear and the inductance parameter is a known constant. 2 Current Orienting and Tracking - Differentiators 2.1 Current Orienting In asynchronous motor control, the commonly used coordinate systems are the stationary or-, coordinate system- and field-oriented dq coordinate systems. This paper proposes a new current-oriented coordinate system, which has unique advantages in terms of steady-state flux linkage estimation and stator resistance identification. The stator current of the asynchronous motor is projected in the space coordinate vector (its rotational speed is called in the a) coordinate system as the defined “1-2 coordinate systemâ€, with its 1 axis and the same direction and behind the 2 axes of 71/2, And the rotation speed of the coordinate system is equal to the code. In this paper, subscripts 1 and 2 are used to distinguish the projection of each vector on the 1 and 2 axes. Current-oriented 1-2 coordinate system The transformation relationship between currents in the 1-2 coordinate system and the coordinate system also has the same conversion relationship as the magnetic flux chain. If yes, b=ZL, then the mathematical model of the electromagnetic system of the asynchronous motor can be written as a ruler, 'is a rotor resistance; 4, B is a stator rotor inductance; 7;=V' is a rotor time constant; 4 is a rotor's mutual inductance. ;For the motor speed. The rotation speed code of the coordinate system (that is, 1) is only related to the stator current. If the current sampling period is t, there is measurement noise, and it is difficult to give a high-quality differential signal in equation (4). - The tracking-differentiator can accurately calculate the call (magic, to achieve current orientation, 快速P's fast control of the optimal function is m jc2,r,/,,,/ice) as a nonlinear function, then the discrete system j: ! (/:+1)=! (k) +hx2 "Track-Differentiator (TD)" called signal vk), its output; differential for vt). Example: Given the signal: differential results of ysinGroWO and y2=sin(2jislave)+vv(A:/i) where vv(A) is noise. Obviously, using TD gives a high-quality differential, while a purely differentiator amplifies the noise in 3, submerging the effective signal. 3 Steady State Rotor Flux Estimation and Stator Resistance Identification Unless otherwise specified, the symbols in this section are based on the 1-2 coordinate system. Based on Eq. (2), cancellation and acquisi- tion can be obtained without the rotor time constant and motor speed, and only the stator resistance is included in Eq. (8). Relative to the time constant of the electromagnetic subsystem, the ruler changes very slowly with temperature, so when examining the flux linkage equation, it can be approximated that the brother is constant. When the flux linkage reaches the steady state, if the formula (9) is available, if the order is %=(2+)/2, then the equation is developed in steady state, and the basic equation of the rotor voltage is substituted, and t=-Ki can be obtained. Â¥nin+Â¥niri) ((3) The sum is the two components of the rotor current, and substituting the above two formulas into (13) yields the solution of equation (14). In the case of (15), the sign depends only on the phase relationship of the stator voltage Mm in the coordinate system, and when it is current, the sign is taken, and vice versa, the sign is “+.†Therefore, the equation (15) can be rewritten as (8) The two equations above are the steady-state rotor flux estimator and the stator resistance identifier, which are independent of the initial value of the flux linkage, do not include the rotor resistance and rotation speed, and only use a certain steady state. The momentary stator voltage and current can be used to calculate the rotor flux. This outstanding advantage is due to the use of current orientation.4 Improved UI Method Rotor Flux Estimator The symbols in this section are based on the standard, unless otherwise specified. The classical UI method of rotor flux linkage estimator is the unknown stator flux initial value (or remanence), 圮 is the stator resistance setpoint, and W represents the noise introduced by the measurement noise. Using the conclusion in Section 3, we can rewrite Equation (21). Assuming that the electromagnetic subsystem is at steady-state at time r, then the estimated values ​​of the rotor flux and stator resistance at this time can be obtained according to equations (11), (16), and (18), then the rotor flux estimation after r can be written as WrLlÂ¥s-khh (22) is an improved UI rotor flux estimator. The first and the second are the estimated values ​​of stator flux and stator resistance at r. Compared with (21), Equation (22) has the following three improvements: (1) The online identification result of the stator resistance replaces the set value and reduces the influence of the stator resistance perturbation. Stator flux steady-state estimates replace the unknown remanence, making the initial value of the integral more accurate. r becomes the new starting point for the measurement. The accumulation of measurement noise is shortened from (0,0 to (r,0) to effectively suppress the integral drift. From the discrete forms of equations (21) and (22), we can also see improvements in these aspects, as shown in equations (23) and (24). Improve the block diagram of the ui method, as shown. It can be seen that the improved UI method has a steady-state flux linkage correction (or feedback) with a closed-loop structure. 5 Application and analysis 69.31mH, stator and rotor inductance is L, =Z=71.31mH, initial value of stator resistance is = 0.4350. Use (a) curve to simulate the perturbation of stator resistance. The motor runs under vector control. The flux linkage and speed commands are 0.8Wb and 2.5Hz, respectively. The voltage and current signals contain a certain amount of measured noise. (b) Given the identification error of the stator resistance identifier, (c) and (d) give the angular estimation error and amplitude estimation error of the UI method and the improved UI method flux linkage estimator, respectively. Obviously, the estimation error of the UI method is affected by the perturbation of the stator resistance, and it gradually diverges with the accumulation of noise. The improved UI method flux linkage estimator, based on the identification of stator resistance and suppression of noise accumulation, has an estimated error that is small and does not diverge. Stator Resistance Variation Stator Resistance Identification Error Flux Angle Estimate Error Flux Amplitude Estimation Error Simulation Results Conclusion In this paper, the current-oriented coordinate system was established using the track-differentiator, and the rotor flux linkage of the induction motor was explored as a new entry point. Estimating the problem and proposing an improved ui method. The new method is equipped with a stator resistance identification capability that suppresses the accumulation of measurement noise and can therefore be adapted to certain uncertainties in the system. In terms of robustness, the improved UI method is the same as the UI method and is superior to the 1-) method and the U-co method. In addition, compared with EKF, MRAS and other methods, the improved UI algorithm is simple and easy to implement. Simulation research shows that the improved ur method has high precision and is suitable for asynchronous motor control systems without speed sensor. Black Wire Mesh,Black Metal Mesh,Black Wire Fence,Black Metal Wire Mesh ANPING HONGYU WIREMESH CO.,LTD , https://www.hongyu-wiremesh.com