Speed Sensorless Control Of Induction Motor Based On SSTEKF

In the speed regulation of modern high-performance asynchronous motors,the speed detection is the key link to realize the precise control of the rotor speed.The speed is generally obtained by an encoder or other position sensor.However,if a mechanical position sensor is installed,because the sensor signal is easily interfered and the concentricity problem of the sensor during installation,the robustness and reliability of the system will be affected,and the sensor is easily damaged in harsh environments and increases the drive system’s cost.In addition,asynchronous motor drive systems require additional installation space.Therefore,the development of a speed sensorless identification method for asynchronous motors with high accuracy,wide speed range,and strong robustness has always been a research hotspot.However,the traditional speed sensorless vector control system has strong parameter sensitivity and weak ability to deal with internal and external interference.Therefore,in view of the problems existing in the traditional speed sensorless speed identification,the work done in this paper is as follows:First of all,when the asynchronous motor is running at medium and high speed,there is a lot of noise in the system.The Kalman Filter(KF)identification method,which has a good effect on noise suppression,is often used in linear systems,while the extended Kalman Filter(EKF)non-speed transmission method is used in non-linear systems such as asynchronous motors.Sensing technology identifies the speed,and the good speed following performance is verified by simulation.In order to improve the problem of electrical parameter disturbance caused by low speed estimation accuracy and interference from the external environment when the electrical parameters of the uncontrollable motor in the running process of the traditional extended Kalman filter speed sensorless vector control system change,the Symmetrical Strong Tracking Extended Kalman Filter(SSTEKF)algorithm.Due to the torque ripple phenomenon in asynchronous motors during operation,a new type of flux linkage observer is proposed based on the flux linkage estimation method of voltage model and current model,which can provide realtime feedback to more electrical parameters of the motor and further improve.The immunity to motor parameter changes and external input signal changes in the vector control of induction motors is verified.The final simulation results verify the effectiveness of the proposed method,and the Routh criterion is used in the article to prove its stability in the global range.For strong tracking extended kalman filtering Algorithm based on the symmetry of the asynchronous motor without speed sensor in the system for noise sensitivity is very high,very small error will cause the model to estimate the problem of inaccurate,by offline parameter identification method,strong tracking extended kalman Algorithm for symmetry inherent noise covariance matrix Q and R matrix parameters optimization,after comparing with the improved Whale Optimization Algorithm(WOA)to identify the parameters,and the simulation show that the improved Algorithm whales quicker to close to the actual motor parameter and high precision.At last,the dead-zone compensation strategy is adopted to solve the problem that the output voltage deviates from the reference voltage due to the dead-zone time,resulting in a large number of voltage harmonics and current harmonics.Greatly improve the power grid harmonic noise problems,further improve the caused by harmonic current of asynchronous motor speed jitter problem without speed sensor system,this paper adopts the strategy of the side of the stator current compensation,finally the simulation verify the effectiveness of the method,by improving the motor current in running due to the problem of distortion of the noise and make motor can reduce the noise interference in the context of the given speed.